VITA (Sept, 2019)

Rand R. Wilcox

Department of Psychology3620 S. McClintock AveUniversity of Southern CaliforniaLos Angeles, CA 90089-1061(213) 740-2258FAX: 213-746-9082email: rwilcox@usc.eduPersonal Web Page: https://dornsife.usc.edu/cf/labs/wilcox/wilcox-faculty-display.cfmIt contains my R functions plus information about my books, including downloadable material.

Member of the USC Consortium on Data-Driven Human Performance andPersonalized Interventions (HAPPI)https://dornsife.usc.edu/labs/consortium-for-human-performance-informati

Software: My R functions are also available via the WRS package(Wilcox, R. & Schonbrodt, F. D., 2016). It can be retrieved fromhttps://github.com/nicebread/WRSOr download the functions fromhttps://figshare.com/articles/R functions for applying robust statistical methods/4833134A subset of my R functions is available at CRANS (Mair & Wilcox, in press);install the R package WRS2.

EDUCATIONPhD 1976, University of California, Santa Barbara

Educational Psychology (Statistics & Psychometrics)MA 1976, University of California, Santa Barbara

Applied Mathematics & StatisticsBA 1968, University of California, Santa Barbara

Mathematics

PROFESSIONAL EXPERIENCEProfessor, University of Southern California,

1987-presentAssociate Professor, University of Southern California,

1983-1987

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Assistant Professor, University of Southern California,1981-1983

Senior Research Associate, Center for the Study of Evaluation,University of California, Los Angeles,1976-1981

PROFESSIONAL ASSOCIATIONSAmerican Statistical AssociationAssociation for Psychological ScienceInternational Association of Statistical ComputingInternational Statistical Institute

EDITORIAL AND PROFESSIONAL SERVICEElected council member of the IASC. Term: 2009-2013.Editorial Board Member:

Psychometrika (Associate Editor, 1983-2018)Computational Statistics and Data Analysis (Associate Editor, 1998-2019)Advisory Board (2006) for a Sage book on Quantitative MethodsCommunications in Statistics–Theory and Methods(Associate Editor, 2006-present)Communications in Statistics–Simulation and Computation(Associate Editor, 2006-present)Communications in Statistics– Case Studies and Data Analysis(Associate Editor, 2016-present)Annals of Phytomedicine (Foreign Editor, 2014-present)Editorial Board, Handbook Series on Computing and Statisticswith Applications. Elsevier.(2007-2010)British Journal of Mathematical andStatistical Psychology (2002-present)Journal of Experimental Education (Consulting Editor, 2006-2009)Psychological Methods (2001-2006)Applied Psychological Measurement (1980-1996)The American Journal of Mathematical & Management Sciences (1981-2011)Journal of Educational Measurement (1981-1989)Journal of Personality and Social Psychology (1991-1996)

NIMH’s standing committee that reviews interventions/clinicaltrials that target all adult disorders.NIH review panel: proposals dealing with MSMNIH review panel: proposals dealing with biomarkers (2012)

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NIMH’s ITVA: standing committee that reviews interventions/clinicaltrials that target all adult disorders (2013).Invited committee member, special issue of Colombian Journal of Statistics

Editorial Reviews:American Educational Research AssociationAmerican StatisticianJournal of Educational and Behavioral StatisticsJournal of Educational MeasurementJournal of Educational PsychologyApplied Psychological MeasurementPsychological BulletinBritish Journal of Mathematical and Statistical PsychologyBritish Journal of PsychologyPsychometrikaJournal of Personality and Social PsychologyEducational Evaluation and Policy AnalysisJournal of the American Statistical AssociationJournal of Mathematical PsychologyPsychological ScienceJournal of Official StatisticsMarketing ScienceAmerican Journal of EpidemiologyComputational Statistics & Data AnalysisAmerican Public Health AssociationJournal of Quality TechnologyJournal of Experimental EducationThe European Journal of Cognitive PsychologyReview of Educational ResearchJournal of Nonparametric StatisticsPsychological MethodsBehavior Research Methods, Instruments & ComputersBehavior Research MethodsCommunications in Statistics–Theory and MethodsPerceptual and Motor Skills Psychological ReportsStatistical PapersBiometrical JournalPsychological ReviewIsraeli Science FederationJournal of Clinical Child and Adolescent Psychology

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The Statistician (Journal of the Royal Statistical Society, Series D)Journal of Applied StatisticsJournal of Multivariate AnalysisApplied Sequential MethodologiesJournal of Statistical Computation and SimulationBMC BioinformaticsOrganizational Research MethodsSouth African Statistical JournalApplied LinquisticsJournal of ClassificationAmerican PsychologistMethodology JournalJournal of Cerebral Blood Flow & MetabolismBMC Medical Research MethodologyPsicologica. International Journal of Methodology and Experimental PsychologyJournal of Probability and StatisticsJournal of Statistical Planning and InferenceJournal of Neuroscience MethodsBehavioral Ecology and SociobiologyPakistan Journal of StatisticsTechnometricsInformation SciencesAustrian Science FundCognitive Therapy and ResearchEconometrics (MDPI Open Access)Mathematical Problems in EngineeringNational Science Center, Poland (review grant proposal).Journal Engineering and Science (Ingeniera y Ciencia)Journal of Aerospace Engineering2013 State Natural Science Award of the People’s Republic of China.Modern Applied ScienceStatistics and Probability LettersInternational Journal of Environmental Research and Public HealthJournal of Physiological AnthropologyEuropean Journal of NeuroscienceMathematical and Computational ApplicationsClinical EpidemiologyComputer Methods and Programs in BiomedicineCogent PsychologyDesigns

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Journal of Mathematical and Fundamental SciencesNeuroImageIBR ReportsBehavioural Brain ResearchBMC MedicineBMC Medical Research MethodologyPsychological Reports

HONORSElected member of the International Statistical InstituteCenter for Excellence in Research fellow, 2007-2010 (Three year term).Thomas L. Saaty Award for best applied paper appearing in

the American Journal of Mathematical and Management Sciences, 1983Teaching/mentor award from the graduate association of

students in psychology at USCAPS (Association for Psychological Science) fellow, 1989-presentAPA (American Psychological Association) fellow, 1982-1989Listed in IBC 2000 Outstanding Intellectuals of the 21st Century,

IBC Leading Educators of the World, Who’s Who in the World,Who’s Who in the West, Who’s Who in Science and Technology,Who’s Who in Science and Engineering,Who’s Who in American Education, United Who’s Who,Madison’s Who’s Who Among Executives and ProfessionalsMadison’s Who’s Who Among Executives and Professionals Honors Edition 2008-2009Madison’s Who’s Who 2008-2009 Honors EditionWho’s Who in Social Sciences Higher Education, Who’s Who in America.Who’s Who in America 2009, 60th Diamond Edition, Who’s Who in medicine andhealth care, Who’s Who in America 2010American Men and Women of Science70th Platinum Anniversary Edition of Who’s Who in America

GRANTSNIE Basic Skills Grant ($57,000)

Full-time financial support from NIE, 1976-1981Part-time NIE support from 1981-1983.

Faculty Research and Innovation Fund Grant from USC ($8,300)Travel Grant from the Psychometric Society ($500)DOD ($595,690: 2014-2018) Jill NcNitt-Gray is the PI.

I handle the data analyses.University of Oregon (PAC-12 Student-Athlete and Well-Being Grant Program

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($109,484: 2017-2018. Jill NcNitt-Gray is the PI. I handle the data analyses.DOD Spinal Cord Injury Model System (funded for 5 years, 2017-2022)

Sara J. Mulroy and Philip Requejo are the PIs. I handle the data analyses.NIDILRR Field Initiated Projects Research Oct 2017-2020

Sara J. Mulroy and Philip Requejo are the PIs. I handle the data analyses.

ADMINISTRATIVE DUTIESAssociate Chair, 2002-2003Associate Chair, 1993.Head of Quantitative, 1997-2005Center for Excellence in Research Fellow, 2007-2010.(Organized the Interdisciplinary Statistics Group)Served on various departmental committees(e.g., computer accounts, awards, chair search)Scientific misconduct committee (2009).

BOOK REVIEWSThe statistical implications of pre-test and Stein-ruleestimators in econometrics. By G. Judge & M. B. BockTechnometrics, 1979, 21, 389-390.

Selecting and ordering populations: A new statisticalmethodology By G. Gibbons, I. Olkin & M. SobelPsychometrika, 1980, 45, 285.

Criterion-referenced measurement: The state of the art.Edited by R. Berk. Applied Psychological Measurement,1981, 5, 133-135.

INVITED TALKS AND WORKSHOPS :CDC (Center for Disease Control)Harvard (Dept of Statistics)Stanford (Psychology)Montana State (Statistics)University of Geneva (Economics and Education)Rand Corp.2nd international conference on multiple comparisonsUniversity of Wisconsin (Psychology and Education)University of Oklahoma (Psychology)University of California Santa Barbara

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2003 APA Conference2004 AERA Conference2004 Midwest Biopharmeceutical Conference2005 APS ConferenceKeynote address at the third International Association of

Statistical Computing world conference, 2005.Workshop given at the 2006 APS conference.Workshop (Fall, 2007) given at the Dutch-Flemish Research School

Experimental PsychoPathology, which is a collaboration of 8 Dutch andBelgium University institutes/departments.

University of Maastricht, 2007JSM (Joint Statistics Meeting, American Statistical Association), 2008Georgia Tech (Dept. of Engineering), 2009Institute for Health Metrics and Evaluation, Univ. of Wash. 2010University of California, Riverside (Dept of Psychology), May, 2011Workshop: Robust Methods for Personality and Individual Differences

Bertinoro, Italy, July, 2011 supported by EAPP and ISSID.Acadia University, Nova Scotia (Dept. of Statistics), 2011.University of California, Riverside (Dept. of Statistics), Oct. 2011Dept. of Psychology, University of Manitoba, March, 2012COMPSTAT (IASC) 2012, Cyprus, August, 2012Keynote address and workshop, University of Hong Kong, Dec. 2012Workshop organized by Trent University, May, 2014.Workshop at Bioinformatics, Statistical and Psychological Approaches

for Molecular Genetics Of Personality and its Adaptive Systems, 2015Workshop, Western Psychological Association, 2016Department of Methodology and Statistics at Tilburg University, 2016Psychological Methods Department at the University of Amsterdam, 2016.Keynote address: Netherlands Society for Statistics and Operations Research (VVS-OR), 2016Dept of Psychology, University of Stockholm, 2016

BOOKS

Wilcox, R. R. (1985). Advances in Basic Statistics.Center for the Study of Evaluation, University of California,Los Angeles.

Wilcox, R. R. (1987). New Statistical Procedures for the SocialSciences: Modern Solutions to Basic Problems. Hillsdale, New Jersey: Erlbaum.

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Wilcox, R. R. (1996). Statistics for the Social Sciences.San Diego, CA: Academic Press.

Wilcox, R. R. (1997). Introduction to Robust Estimation and Hypothesis Testing.San Diego, CA: Academic Press.

Wilcox, R. R. (2001). Fundamentals of Modern Statistical Methods:Substantially Improving Power and Accuracy. New York: Springer.

Wilcox, R. R. (2003). Applying Contemporary Statistical Techniques.San Diego: Academic Press.

Wilcox, R. R. (2005). Introduction to Robust Estimation and Hypothesis Testing2nd Edition. San Diego, CA: Academic Press.

Wilcox, R. R. (2009). Basics Statistics: UnderstandingConventional Methods and Modern Insights. New York: OxfordUniversity Press

Wilcox, R. R. (2010). Fundamentals of Modern Statistical Methods:Substantially Improving Power and Accuracy, 2nd Edition. New York: Springer.

Wilcox, R. R. (2012). Modern Statistics for the Social andBehavioral Sciences: A Practical Introduction.New York: Chapman & Hall/CRC pressISBN: 978-1-4398-3456-5

Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing3rd Edition. San Diego, CA: Academic Press.

Wilcox, R. R. (2017). Understanding and Applying BasicStatistical Methods Using R. New York: Wiley.ISBN: 978-1-119-06139-7http://www.wiley.com/WileyCDA/WileyTitle/productCd-1119061393.html

Wilcox, R. R. (2017). Introduction to Robust Estimation and Hypothesis Testing4th Edition. San Diego, CA: Academic PressISBN: 978-0-12-386983-8https://www.elsevier.com/books

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Wilcox, R. R. (2017). Modern Statistics for the Social andBehavioral Sciences: A Practical Introduction 2nd Ed.Boca Raton, FL: Chapman & Hall/CRC pressISBN: 978-1-4987-9678-1 Cat #: K30405

PUBLICATIONS

Wilcox, R. R. (1976). A note on the length and passing score of a mastery test.Journal of Educational Statistics 1, 359-364

Wilcox, R.R. & Harris, C. W. (1977). On Emrick’s “An evaluation modelfor mastery testing.” Journal of Educational Measurement, 14, 215-218.

Wilcox, R. R. (1977). New methods for studying stability. In C. W.Harris, A. Pearlman, and R. Wilcox, Achievement test itemsmethods of study, CSE Monograph No. 6, Los Angeles:Center for the Study of Evaluation, University of California.

Wilcox, R. R. (1977). New methods for studying equivalence. InC. W. Harris, A. Pearlman and R. Wilcox,Achievement test items - methods of study. In CSEMonograph No. 6, Los Angeles: Center for the Study ofEvaluation, University of California,

Wilcox, R. R. (1977). Estimating the likelihood of a false-positiveor a false-negative decision with a mastery test:An empirical Bayes approach. Journal of Educational Statistics,2, 289-307.

Wilcox. R. R. (1978). Estimating true score in the compound binomialerror model. Psychometrika, 43, 245-258.

Wilcox, R. R. (1978). Some comments on selecting the best ofseveral binomial populations or the bivariate normalpopulation having the largest correlation coefficient.Psychometrika, 43, 127-128.

Wilcox, R. R. (1978). A note on decision theoretic coefficients fortests. Applied Psychological Measurement, 1978,2, 609-613.

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Wilcox. R. R. (1979). Comparing examinees to a control.Psychometrika, 44, 55-68.

Wilcox, R. R. (1979). Applying ranking and selection techniques todetermine the length of a mastery test. Educationaland Psychological Measurement, 39, 13-22.

Wilcox, R. R. (1979). Estimating the parameters of the beta-binomialdistribution. Educational and Psychological Measurement,39, 527-535.

Wilcox, R. R. (1979). An alternative interpretation of three stabilitymodels. Educational and Psychological Measurement,39, 311-316.

Wilcox, R. R. (1979). Achievement test items and latent structure modelsBritish Journal of Mathematical and Statistical Psychology 32, 61-71.

Wilcox, R. R. (1979). A lower bound to the probability of choosing theoptimal passing score for a mastery test when there is anexternal criterion. Psychometrika, 44, 245-249.

Wilcox, R. R. (1979.) A two-stage procedure for selecting the bestof several binomial populations. Educational andPsychological Measurement, 39, 715-725.

Wilcox. R. R. (1979). Upper and lower bounds to the probabilityof a false-positive or false-negative decision with a masterytest. Journal of Educational Statistics, 4, 59-73.

Wilcox, R. R. (1980). Some exact sample sizes for comparing the squaredmultiple correlation coefficient to a standard.Educational and Psychological Measurement, 40, 119-125.

Wilcox, R. R. (1980). Some results and comments on using latent structuremodels to measure achievement. Educational and PsychologicalMeasurement, 40, 119-125.

Wilcox. R. R. (1979). Prediction analysis and the reliability of a mastery

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test. Educational and Psychological Measurement,39, 825-839. Reprinted, with permission,in E. Baker & E. Quellmalz (Eds.), Educational testing and evaluation.Beverly Hills: Sage, 1980.

Harris, C. W. & Wilcox, R. R. (1980). Brennan’s B is Peirce’s theta.Educational and Psychological Measurement, 40, 307-311.

Wilcox, R. R.(1980). Determining the length of a criterion-referenced testApplied Psychological Measurement, 4, 425-446.

Wilcox, R. R. (1980). An approach to measuring the achievement orproficiency of an examinee. Applied Psychological Measurement, 4, 241-251.

Wilcox, R. R. (1980). An empirical comparison of four discretediscriminant analysis procedures. Educational andPsychological Measurement, 40, 981-986.

Wilcox, R. R. (1981). Selecting the best t of k examinees whose truescore is better than a standard. Educational andPsychological Measurement, 41, 709-716.

Wilcox, R. R. (1981). The single administration estimate of theproportion of agreement when a test is scored with a latent structuremodel. Educational and Psychological Measurement, 41, 389-400.

Wilcox, R. R. (1981) A review of the beta-binomial model and itsextensions. Journal of Educational Statistics, 6, 3-32.

Wilcox. R. R. (1981). Methods and recent advances in measuringachievement: A response to Molenaar. British Journal ofMathematical and Statistical Psychology, 34, 229-237.

Wilcox. R. R. (1981). Analyzing the distractors of multiple-choicetest items or partitioning cell probabilities with respect to astandard. Educational and Psychological Measurement, 41, 1051-1068.

Wilcox, R. R. (1981). A cautionary note on estimating the reliabilityof a mastery test with the beta-binomial model.Applied Psychological Measurement, 5, 531-537.

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Wilcox, R. R. (1981). Solving measurement problems with an answeruntil correct scoring procedure. Applied Psychological Measurement, 5, 399-414.

Wilcox, R. R. (1981). A closed sequential procedure for comparing thebinomial distribution to a standard. British Journal ofMathematical and Statistical Psychology, 34, 238-242.

Wilcox, R. R. (1982). Some empirical and theoretical results on ananswer-until-correct scoring procedure. British Journal ofMathematical and Statistical Psychology, 35, 57-70.

Wilcox, R. R. (1982). Determining the length of multiple-choicecriterion-referenced tests when an answer-until-correct scoringprocedure is used. Educational and Psychological Measurement, 42, 789-794.

Wilcox, R. R. (1982). Using k out of n system reliability to study andcharacterize tests. Educational and Psychological Measurement, 42, 153-165.

Wilcox, R. R. (1982). A closed sequential procedure for answer-until-correct tests. Journal of Experimental Education, 50, 219-222.

Wilcox, R. R. (1984) Simulation as a research technique. InternationalEncyclopedia of Education, Husen, T. & Postlethwaite, T. (Eds.)

Wilcox, R. R. (1982). True score models. International Encyclopedia ofEducation, 5302-5304.

Wilcox, R. R. (1982). Bounds on the k out of n reliability of atest, and an exact test for hierarchically related items.Applied Psychological Measurement, 6, 67-74.

Wilcox, R. R. (1982). Some new results on an answer-until-correctscoring procedure. Journal of Educational Measurement, 19, 67-74.

Wilcox, R. R. (1982). A comment on approximating the X2

distribution in the equiprobable case. Communications in Statistics –Simulation and Computation, 11, 619-623.

Wilcox, R. R. (1982). On a closed sequential procedure for categorical

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data, and tests for equiprobable cells. British Journal ofMathematical and Statistical Psychology, 35, 193-207.

Wilcox, R. R. (1985). Partitioning populations with respect to a standard or control,with an emphasis on problems in mental test theory. In The frontiers ofmodern statistical inference procedures. Columbus: American Sciences Press.

Wilcox, R. R. (1982). On discrete discriminate analysis. Proceedingsof the 24th Annual Military Testing Association Conference, pp. 13-18.

Wilcox, R. R. (1983). How do examinees behave when taking multiplechoice test items? Applied Psychological Measurement, 7, 239-240.

Wilcox, R. R. (1983). Approximating the probability of identifying themost effective treatment for the case of normal distributionshaving unknown and unequal variances. Educational andPsychological Measurement, 43, 43-51.

Wilcox, R. R. (1983). An approximation of the k out of n reliability of atest, and a scoring procedure for determining which items anexaminee knows. Psychometrika, 48, 211-222.

Wilcox, R. R. (1983). A table of percentage points of the range ofindependent t variables. Technometrics, 25, 201-204.

Wilcox, R. R. (1983). Approximating the probability of selecting the besttreatment with a heteroscedastic procedure when the first stagehas unequal sample sizes. Journal of Educational Statistics,8, 45-58.

Wilcox, R. R. (1983). Unbiased estimation in a closed sequential testingprocedure. Educational and Psychological Measurement,43, 1061-1063.

Wilcox, R. R. (1983). A simple model for diagnostic testing when thereare several types of misinformation. Journal of ExperimentalEducation, 52, 57-62.

Wilcox, R. R. (1984). A table for Rinott’s selection procedure.Journal of Quality Technology, 16, 97-100.

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Wilcox, R. R. (1983) Measuring mental abilities with latent statemodels. American Journal of Mathematical and ManagementSciences, 3, 313-345.

Wilcox, R. R. (1984). A review of exact hypothesis testing procedures(and selection techniques) that control power regardless of thevariances. British Journal of Mathematical and StatisticalPsychology, 37, 34-48.

Wilcox, R. R. (1984). A note on measuring item bias. Journal ofExperimental Education, 53, 114-116.

Wilcox, R. R. (1984). On two-stage multiple comparison procedures whenthere are unequal sample sizes in the first stage.Journal of Educational Statistics, 9, 227-236.

Wilcox, R. R. (1984). Selecting the best population provided it isbetter than a standard. Journal of the American Statistical Association,79, 887-891.

Wilcox, R. R. (1985). On comparing treatment effects to a standardwhen the variances are unknown and unequal.Journal of Educational Statistics, 10, 45-54.

Wilcox, R. R. (1985). Percentage points of the product of two correlatedt variates. Communications in Statistics–Simulation and Computation, 14, 143-157.

Wilcox, R. R. (1985). An extended and slightly improved table of criticalvalues for testing q linear contrasts in a repeated measuresdesign. Communications in Statistics– Simulation and Computation, 14, 55-70.

Wilcox, R. R. (1985). Strong true score theory. Encyclopediaof Statistical Sciences, New York: Wiley, 9, 17-19.

Wilcox, R. R.(1985). Estimating the validity of a multiple-choicetest item having k correct alternatives. AppliedPsychological Measurement, 9, 311-316.

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Wilcox, R. R. & Chao, E. (1985) Planned Comparisons: Controlling powerand determining sample sizes. British Journal of MathematicalStatistical Psychology, 38, 216-221.

Wilcox, R. R.(1985). On a Stein-type two-stage procedure for thegeneral linear model. British Journal of Mathematicaland Statistical Psychology, 38, 222-226.

Wilcox, R. R. (1986). Measuring achievement with latent structuremodels. In Alternative approaches to the assessmentof achievement, D. McArthur (Ed.)Boston: Kluwer-Nijhoff., 157-186.

Wilcox, R. R. (1987). New designs in the analysis of variance.Annual Review of Psychology, 38, 29-60.

Wilcox, R. R. (1986). Controlling power in a heteroscedasticANOVA procedure. British Journal of Mathematical andStatistical Psychology, 39, 65-68.

Wilcox, R. R. (1986). Critical values for the correlated t-testwhen there are missing observations. Communicationsin Statistics–Simulation and Computation, 15, 709-714.

Wilcox, R. R. (1986). Improved simultaneous confidence intervalsfor regression parameters and linear contrasts.Communication in Statistics– Simulation and Computation, 15, 917-932.

Wilcox, R. R. & Charlin, V. L. (1986). Comparing medians:A monte carlo study. Journal of Educational Statistics,11, 263-274.

Wilcox, R. R., Charlin, V. L. & Thompson, K. (1986). Newmonte carlo results on the robustness of the ANOVA F ,W , and F∗ statistics. Communications in Statistics–Simulation and Computation, 15, 933-944.

Wilcox, R. R. (1986). Percentage points of the bivariate t distributionfor non-positive correlations. Metron, 44, 115-119.

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Wilcox, R. R. (1986). On a multiple comparison procedure fordetermining which means are substantially different.American Journal of Mathematical and Management Sciences, 6, 205-218.

Wilcox, R. R. (1987). Pairwise comparisons of J independent regressionlines over a finite interval, simultaneous pairwise comparisonof their parameters, and the Johnson-Neyman technique.British Journal of Mathematical and Statistical Psychology,40, 80-93.

Wilcox, R. R. (1987). A heteroscedastic ANOVA procedure with specifiedpower. Journal of Educational Statistics, 12, 271-281.

Wilcox, R. R. (1987). Confidence intervals for true scores under ananswer-until-correct scoring procedure. Journal of Educational Measurement,24, 263-269.DOI: 10.1111/j.1745-3984.1987.tb00279.x

Wilcox, R. R. (1987). On two-stage multiple comparisons with a control:Handling unequal sample sizes in the first stage.American Journal of Mathematical and Management Sciences, 7, 297-305.

Wilcox, R. R. (1987). Within row pairwise comparisons in a two-waydesign. Communications in Statistics–Simulation and Computation, 16, 939-956.

Wilcox, R. R. & Wilcox, K. T. (1988). Models of decision-makingprocesses for multiple-choice test items:An analysis of spatial ability. Journal of EducationalMeasurement, 25, 125-136.

Wilcox, R. R. (1988). A new alternative to the ANOVA F and new resultson James’s second order method. British Journal ofMathematical and Statistical Psychology, 41, 109-117.

Wilcox, R. R. (1989). Comparing the variances of dependent groups.Psychometrika, 54, 305-316.

Wilcox, R. R., Wilcox, K. T. & Chung, J. (1988). A note ondecision-making processes for multiple-choice

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test items. Journal of Educational Measurement, 25, 247-250.

Wilcox, R. R. (1989). Percentage points of a weighted Kolmogorov-Smirnov statistic. Communications in Statistics–Simulation and Computation, 18, 237-244.

Charlin, V. L. & Wilcox, R. R. (1989). An algorithm for comparingmedians. Psychometrika, 54, 345-348.

Wilcox, R. R. (1989). Adjusting for unequal variances when comparingmeans in one-way and two-way fixed effects ANOVA modelJournal of Educational Statistics, 14, 269-278.

Wilcox, R. R. (1990). Determining whether an experimental group isstochastically larger than a control. BritishJournal of Mathematical and Statistical Psychology,43, 327-333.

Wilcox, R. R. (1990). Comparing the means of two independentgroups Biometrical Journal, 32, 771-780.

Wilcox, R. R. (1990). Comparing variances and means when distributionshave non-identical shapes. Communications in Statistics –Simulation and Computation, 19, 155-173.

Wilcox, R. R. (1991). Comparisons with a control in two-way and one-way designs, and determining whether the most effectivetreatment has been selected with probability 1-α.British Journal of Mathematical and Statistical Psychology,43, 93-112.

Wilcox, R. R. (1990). Comparing the variances of two dependent groups.Journal of Educational Statistics, 15, 237-247.

Wilcox, R. R. (1991). Testing whether independent treatment groups haveequal medians. Psychometrika, 56, 381–396

Wilcox, R. R. (1992) An improved method for comparing variances whendistributions have non-identical shapes. ComputationalStatistics and Data Analysis, 13, 163–172.

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Wilcox, R. R. (1990). Comparing biweight measures of location in the two-sample problem. Communications in Statistics–Simulation and Computation, 19, 1231-1246.

Wilcox, R. R. (1991). A step-down heteroscedastic multiple comparisonprocedure. Communications in Statistics– Theory and Methods,20, 1087-1097.

Wilcox, R. R. (1991). Comparing biweight measures of location.The Statistician, 40, 393–399

Wilcox, R. R. (1991). Nonparametric analysis of covariance based onpredicted medians. British Journal of Mathematical andStatistical Psychology, 44, 221-230.

Wilcox, R. R. (1992). Comparing one-step M-estimators of locationcorresponding to two independent groups. Psychometrika,57, 141–154

Wilcox, R. R. (1991). Bootstrap inferences about the correlationand variances of paired data. British Journal ofMathematical and Statistical Psychology, 44, 379–382.

Wilcox, R. R. (1993). Robustness in ANOVA. In L. Edwards (Ed.)Applied Analysis of Variance in the Behavioral Sciencespp. 345–374. New York: Marcel Dekker.

Wilcox, R. R. (1992). Comparing robust regression lines corresponding totwo independent groups. Communications in Statistics–Theory and Methods, 21, 1255–1266.

Wilcox, R. R. (1992). Comparing the medians of dependent groups.British Journal of Mathematical and Statistical Psychology, 45, 151–162.

Wilcox, R. R. (1992). Robust generalizations of classical test reliabilityand Cronbach’s alpha. British Journal of Mathematical and StatisticalPsychology, 45, 239–254.

Wilcox, R. R. (1992). Why do conventional methods for comparing means

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have relatively low power, and what can you do to correct the problem?Current Directions in Psychological Science, 1, 101–105.

Wilcox, R. R. (1993). Comparing one-step M-estimators of location whenthere are more than two groups. Psychometrika. 58, 71–78.

Wilcox, R. R. (1994). Simulation as a research technique. InternationalEncyclopedia of Education, 2nd edition

Wilcox, R. R. (1993). Analyzing repeated measures or randomized blockdesigns using trimmed means. British Journal of Mathematicaland Statistical Psychology, 46, 63–76.

Wilcox, R. R. (1993). Comparing the biweight midvariances of twoindependent groups. The Statistician, 45, 29–35.

Wilcox, R. R. (1993). Some results on a Winsorized correlationcoefficient. British Journal of Mathematical and StatisticalPsychology, 46, 339–349.

Wilcox, R. R. (1995). ANOVA: The practical importance of heteroscedasticmethods, using trimmed means versus means, and designing simulation studies.British Journal of Mathematical and Statistical Psychology, 48, 99–114.

Wilcox, R. R. (1994). Some results on the Tukey-McLaughlin and Yuenmethods for trimmed means when distributions are skewed.Biometrical Journal, 36, 259–273.

Wilcox, R. R. (1994). A one-way random effects model for trimmed means.Psychometrika, 59, 289–306.

Wilcox, R. R. (1994). Estimating Winsorized correlations in a univariateor bivariate random effects model. British Journal of Mathematicaland Statistical Psychology, 47, 167–183.

Wilcox, R. R. (1994). The percentage bend correlation coefficient.Psychometrika, 59, 601–616.

Wilcox, R. R. (1994). Computing confidence intervals for the slope ofbiweight midregression and Winsorized regression lines. British

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Journal of Mathematical and Statistical Psychology, 47, 355–372.

Wilcox, R. R. (1995). Three multiple comparison procedures for trimmedmeans. Biometrical Journal, 37, 643–656.

Wilcox, R. R. (1995). Testing the hypothesis of independence betweentwo sets of variates. Multivariate Behavioral Research, 30,213–226.

Wilcox, R. R. (1995). Comparing two independent groups using multiplequantiles. Journal of the Royal Statistical Society. Series D (Statistician), 44, 91–99.

Wilcox, R. R. (1995). Simulation results on solutions to themultivariate Behrens-Fisher problem using trimmed means.The Statistician, 44, 213–225.

Wilcox, R. R. (1995). ANOVA: A paradigm for low power and misleadingmeasures of effect size? Review of Educational Research.65, 51–77.

Wilcox, R. R. (1995). A regression smoother for resistant measures oflocation and scale. British Journal of Mathematical andStatistical Psychology, 48, 189–205.

Wilcox, R. R. (1995). Some small-sample results on a bounded influencerank regression method. Communications in Statistics – Theory and Methods,24, 881–888.

Wilcox, R. R. (1996). Estimation in the simple linear regression modelwhen there is heteroscedasticity of unknown form. Communications inStatistics–Theory and Methods, 25, 1305–1324.

Wilcox, R. R. (1996). Confidence intervals for the slope of a regressionline when the error term has non-constant variance. ComputationalStatistics and Data Analysis, 22, 89–98.

Wilcox, R. R. (1996). A note on testing hypotheses about trimmed means.Biometrical Journal, 38, 173–180.

Wilcox, R. R. (1996). A review of some recent developments in robust

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regression. British Journal of Mathematical and Statistical Psychology,49, 253–274

Wilcox, R. R. (1998). Multivariate Behrens-Fisher problem.Encyclopedia of Statistical Sciences, (update)2. New York: Wiley, pp. 422–426.

Wilcox, R. R. (1996). Confidence intervals for two robust regression lineswith a heteroscedastic error term. British Journal of Mathematicaland Statistical Psychology, 49, 163–170.

Wilcox, R. R. (1997). Some practical reasons for reconsidering theKolmogorov-Smirnov test. British Journal of Mathematicaland Statistical Psychology, 50, 9–20.

Wilcox, R. R. (1997). A bootstrap modification of the Alexander-GovernANOVA method, plus comments on comparing trimmed means. Educationaland Psychological Measurement, 57, 655–665.

Wilcox, R. R. (1997). ANCOVA based on comparing a robust measure oflocation at empirically determined design points. BritishJournal of Mathematical and Statistical Psychology, 50,93–103.

Wilcox, R. R. (1998). Trimming and Winsorizing. Encyclopedia ofBiostatistics. New York: Wiley.

Wilcox, R. R. (1998). The Kolmogorov-Smirnov test. Encyclopedia ofBiostatistics. New York: Wiley.

Wilcox, R. R. (1997). Comparing the slopes of two independent regressionlines when there is complete heteroscedasticity. BritishJournal of Mathematical and Statistical Psychology,50, 309–317.

Wilcox, R. R. (1997). Tests of independence and zero correlationamong p random variables. Biometrical Journal. 39, 183–193.

Wilcox, R. R., Keselman, H. J. & Kowalchuk, R. K. (1998). Can testsfor treatment group equality be improved? The bootstrap and trimmed

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means conjecture. British Journal of Mathematical and StatisticalPsychology, 51, 123–134.DOI:10.1111/j.2044-8317.1998.tb00670.x

Wilcox, R. R. (1998). How many discoveries have been lost by ignoringmodern statistical methods? American Psychologist.53, 300–314.Reprinted with permission in J. Miles & B. Stucky (2014).Quantitative Psychology. Sage.

Wilcox, R. R. (1998). The goals and strategies of robust methods.British Journal of Mathematical and StatisticalPsychology, 51, 1-39.

Wilcox, R. R. (1998). Reply to discussants of the goals and strategies ofrobust methods. British Journal of Mathematical and StatisticalPsychology, 51, 55–62.

Wilcox, R. R. (1997). Pairwise comparisons using trimmed means orM-estimators when working with dependent groups. BiometricalJournal, 39, 677–688.

Wilcox, R. R. (1998). Simulations on the Theil-Sen regression estimatorwith right-censored data. Statistics and Probability Letters39, 43–47.

Wilcox, R. R. (1998). A note on the Theil-Sen regression estimatorwhen the regressor is random and the error term is heteroscedastic.Biometrical Journal, 40, 261–268.

Wilcox, R. R. (1998). Simulation results on extensions of the Theil-Senregression estimator. Communications in Statistics– Simulation and Computation,27, 1117–1126.

Wilcox, R. R. (1999). Rank-based tests for interactions in a two-waydesign. Computational Statistics and Data Analysis, 29, 275–284.

Keselman, H. J., Algina, J., Boik, R. J. & Wilcox, R. R. (1999).New approaches to the analysis of repeated measurements.In B. Thompson (Ed) Advances in Social Science Methodology, 5,

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251–268. Greenwich, Connecticut: JAI Press

Wilcox, R. R. (1999). Comments on Stute, Manteiga, and QuindimilJournal of the American Statistical Association, 94, 659-660.

Wilcox, R. R. & Muska, J. (1999). Measuring effect size: A nonparametricanalog of ω2. British Journal of Mathematical and Statistical Psychology, 52, 93–110.

Wilcox, R. R. & Muska, J. (1999). Tests of hypotheses about regressionparameters when using a robust estimator. Communications in Statistics—Theory and Methods, 28, 2201–2212.

Keselman, H. J. & Wilcox, R. R. (1999). The “improved” Brown andForsyth test for mean equality: Some things can’t be fixed.Communications in Statistics—Simulation and Computation, 28, 687–698.

Wilcox, R. R. (1999). Testing hypotheses about regression parameterswhen the error term is heteroscedastic. Biometrical Journal, 4,411–426.

Wilcox, R. R., Keselman, H. J., Muska, J. & Cribbie, R. (2000).Repeated measures ANOVA: Some new results on comparing trimmed means andmeans. British Journal of Mathematical and Statistical Psychology53, 69–82.

Wilcox, R. R. (2000). Some exploratory methods for studying curvaturein robust regression. Biometrical Journal, 42, 335–347.

Wilcox, R. R. (2000). Nonparametric statistics. Encyclopedia of PsychologyOxford University Press.

Keselman, H. J., Kowalchuk, R. K., Algina, J. Lix, L. M. & Wilcox, R. R.(2000). Testing treatment effects in repeated measures designs:Trimmed means and bootstrapping. British Journal of Mathematicaland Statistical Psychology, 53, 175–191.

Keselman, H. J., Algina, J., Wilcox, R. R., Kowalchuk, R. K. (2000).Testing repeated measures hypotheses when covariance matrices areheterogeneous: Revisiting the robustness of the Welch-James test again.Educational and Psychological Measurement, 60, 925–938.

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Keselman, H. J., Wilcox, R. R., Taylor, J. & Kowalchuk, R. (2000).Tests for mean equality that do not require homogeneity of variances:Do they really work? Communications in Statistics–Simulation and Computation.29, 875–895.

Medina, A. M., Margolin, G., & Wilcox, R. R. (2000). Family hostilityand children’s cognitive processes. Behavior Therapy, 31, 667–684.

Wilcox, R. R. (2001). Detecting nonlinear associations plus comments ontesting hypotheses about the correlation coefficient. Journal of Educationaland Behavioral Statistics, 26, 73–84.

Wilcox, R. R. & Muska, J. (2001). Inferences about correlations whenthere is heterosecdasticity. British Journal of Mathematical andStatistical Psychology, 54, 39–47.

Wilcox, R. R. (2001). Modern insights about Pearson’s correlation andleast squares regression. International Journal of Assessment and Selection.9, 195–205.

Wilcox, R. R. (2001). Rank-based tests for interactions in a two-waydesign when there are ties. British Journal of Mathematical and Statistical53, 145–153.

Wilcox, R. R. (2001). Pairwise comparisons of trimmed means for two ormore groups. Psychometrika, 66, 343–356.

Wilcox, R. R. & Keselman, H. J. (2001). Using trimmed means to compareK measures corresponding to two independent groups. MultivariateBehavioral Research, 36, 421–445.

Wilcox, R. R. (2001). Comments on Long and Ervin. American Statistician, 55.374–375.

Wilcox, R. R. & Keselman, H. J. (2002). Power analyses when comparingtrimmed means. Journal of Modern Statistical Methods, 1,24–31.

Wilcox, R. R. (2002). Comparing the variances of independent groups.

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British Journal of Mathematical and Statistical Psychology55, 169–176.

Wilcox, R. R. & Muska, J. (2002). Comparing correlation coefficients.Communications in Statistics—Simulation and Computation,31, 49–59.

Wilcox, R. R. (2002). Multiple comparisons among dependent groups basedon a modified one-step M-estimator. Biometrical Journal, 44466-477.

Keselman, H. J., Wilcox, R. R., Kowalchuk, R. K. & Olejnik, S.(2002).Comparing trimmed or least squares means of two independent skewed populationsBiometrical Journal, 44, 478–489

Wilcox, R. R. (2002). Can the weak link in psychological research be fixed?APS Observer, 15, 11 & 38.

Wilcox, R. R. (2002). Understanding the practical advantages of modernANOVA methods. Journal of Clinical Child and Adolescent Psychology31, 399–412.

Keselman, H. J., Cribbie, R. A. & Wilcox, R. R. (2002). Pairwisemultiple comparison tests when data are nonnormal. Educational andPsychology, 62, 420–434.

Keselman, H. J., Wilcox, R. R., Othman, A. R., & Fradette, K. (2002).Trimming, transforming statistics, and bootstrapping: Circumventing thebiasing effects of heteroscedasticity and nonnormality.Journal of Modern Applied Statistical Methods, 1, 281–287.

Othman, A. R, Keselman, H. J., Wilcox, R. R., Fradette, K., &Padmanabhan, A. R. (2002). A Test of Symmetry.Journal of Modern Applied Statistical Methods, 1, 310–315.

Wilcox, R. R. & Keselman, H. J. (2002). Within groups multiplecomparisons based on robust measures of location.Journal of Modern Applied Statistical Methods, 1, 281–287.

Wilcox, R. R. (2003). Power: Basics, practical problems and possible

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solutions. In J. Schinka and W. Velicer (Eds.),Research Methods in Psychology, Vol. 2, 65–85. New York: Wiley.

Wilcox, R. R. & Keselman, H. J, (2003). Repeated Measures one-wayANOVA based on a modified one-step M-estimator.British Journal of Mathematical and Statistical Psychology, 56, 15–25.

Keselman, H. J., Wilcox, R. R. & Lix, L. M. (2003). A generally robustapproach to hypothesis testing in independent and correlated groups designs.Psychophysiology, 40, 586–596.

Wilcox, R. R. (2003). Two-sample, bivariate hypothesis testing methodsbased on Tukey’s depth. Multivariate Behavioral Research, 38, 225–246.

Wilcox, R. R. (2003). Multiple comparisons based on a modifiedone-step M-estimator. Journal of Applied Statisics, 301231–1241.

Wilcox, R. R. (2003). Inferences based on multiple skipped correlation.Computational Statistics & Data Analyses, 44, 223–236.

Wilcox, R. R. (2003). Approximating Tukey’s Depth. Communicationsin Statistics–Simulation and Computation, 32, 977–985

Wilcox, R. R. & Keselman, H. J. (2003). Modern robust data analysismethods: Measures of central tendency. Psychological Methods, 8,254–274.

Wilcox, R. R. (2003). Multiple hypothesis testing based on the ordinaryleast squares estimator when there is heteroscedasticity. Educationaland Psychological Measurement, 63, 758–764.

Fradette, K., Keselman, H. J., Lix, L., Algina, J., & Wilcox, R. R. (2003)Conventional And Robust Paired And Independent-Samples t Tests: Type I ErrorAnd Power Rates. Journal of Modern Applied Statistical Methods, 2 , Article 22.DOI: 10.22237/jmasm/1067646120

Andel, R., McLearly, C., Murdock, G., Fiske , A., Wilcox, R., & Gatz, M. (2003).Performance on the CERAD word list memory task: A comparison of university-basedand community-based groups. International Journal of Geriatric Psychiatry, 18, 733–739.

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Wilcox, R. R. (2004). Trimming. The Sage Encyclopedia of Social ScienceResearch Methods. Oregon, OH: Sage.

Wilcox, R. R. (2004). Dispersion. The Sage Encyclopedia of Social ScienceResearch Methods. Oregon, OH: Sage.

Wilcox, R. R. (2004). Deviation. The Sage Encyclopedia of Social ScienceResearch Methods. Oregon, OH: Sage.

Wilcox, R. R. (2004). Eta. The Sage Encyclopedia of Social ScienceResearch Methods. Oregon, OH: Sage.

Wilcox, R. R. (2004). Constant. The Sage Encyclopedia of Social ScienceResearch Methods. Oregon, OH: Sage.

Wilcox, R. R. (2004). A multivariate projection-type analogue of theWilcoxon-Mann-Whitney test. British Journal of Mathematical and StatisticalPsychology, 57, 205-213.

Othman, A. R., Keselman, H. J., Padmanabhan, A. R., Wilcox, R. R. & Fradette, K. (2004)Comparing measures of the “typical” score across treatment groups.British Journal of Mathematical and Statistical Psychology, 57, 215–234.

Wilcox, R. R. (2004). Some results on extensions and modifications of theTheil-Sen estimator. British Journal of Mathematical and StatisticalPsychology, 57, 265–280

Wilcox, R. R. (2004). Extension of Hochberg’s two-stagemultiple comparison method. In N. Mukhopadhyay, S. Datta &S. Chattopoadhyay (Eds.) Applied Sequential Methodologies:Real World Examples with Data Analysis. New York: Dekker. pp 371-380

Keselman, H. J., Othman, A. R., Wilcox, R. R. & Fradette, K. (2004).The new and improved two-sample t test. Psychological Science, 15,47–51.

Wilcox, R. R. (2004). Kernel density estimators: An approach tounderstanding how groups differ. Understanding Statistics, 3, 333-348.

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Wilcox, R. R. (2004). Inferences based on a skipped correlation coefficient.Journal of Applied Statistics, 31, 131–144.

Wilcox, R. R. (2004). An extension of Stein’s two-stage method topairwise comparisons among dependent groups based on trimmed means.Sequential Analysis, 23, 63–74

Wilcox, R. R. & Keselman, H. J. (2004). Robust Regression Methods:Achieving small standard errors when there is heteroscedasticity.Understanding Statistics, 3, 349–364.

Othman, A. R., Keselman, H. J., Padmanabhan, A. R., Wilcox, R. R., & Fradette, K.(2004). An Improved Robust Welch-James Test Statistic. In Y. Abu Hassan,A. Baharum, A. I. Mohd. Ismail, H. L. Koh and H. C. Low (Eds.),Integrating Technology in the Mathematical Sciences (pp. 546-554).Pulau Pinang, Malaysia: Universiti Sains Malaysia.

Keselman, H. J., Wilcox, R. R., Algina, J., Fradette, K., and Othman, A.R. (2004). A power comparison of robust test statistics based onadaptive estimators. Journal of Modern Applied Statistical Methods, 3, 27-38

Wilcox, R. R. & Keselman, H. J. (2004). Multivariate Location:Robust estimators and inferences. Journal of ModernApplied Statistical Methods, 3, 2–12.

Wilcox, R. R. (2005). Outlier detection. Encyclopedia of Statistics in Behavioral Science, 3,1494-1497. New York: Wiley.

Wilcox, R. R. (2005). Outliers. Encyclopedia of Statistics in Behavioral Science, 3,1497-1498. New York: Wiley.

Keselman, H. J. & Wilcox, R. R. (2005). Multiple comparison tests:Nonparametric and resampling approaches.Encyclopedia of Statistics in Behavioral Science, 3, 1325–1331 New York: Wiley.

Wilcox, R. R. (2005). M estimators of locationEncyclopedia of Statistics in Behavioral Science, 3, 1109-1110, New York: Wiley.

Wilcox, R. R. (2005). Robust Testing Procedures.Encyclopedia of Statistics in Behavioral Science, 4, 1768-1769. New York: Wiley.

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Wilcox, R. R. (2005). Robustness of standard tests.Encyclopedia of Statistics in Behavioral Science, 4, 1769-1770. New York: Wiley.

Wilcox, R. R. (2005). Winsorized robust measures.Encyclopedia of Statistics in Behavioral Science, 4, 2121-2122. New York: Wiley.

Wilcox, R. R. (2005). Trimmed means.Encyclopedia of Statistics in Behavioral Science, 4, 2066-2067. New York: Wiley.

Wilcox, R. R. (2005). An affine invariant rank-based method forcomparing dependent groups. British Journal of Mathematicaland Statistical Psychology, 58, 33–42.

Wilcox, R. R. (2005). An approach to ANCOVA that allows multiplecovariates, nonlinearity and heteroscedasticity. Educationaland Psychological Measurement, 65, 442–450.

Wilcox, R. R. (2005). Estimating the conditional variance of Y , given X, in asimple regression model. Journal of Applied Statistics, 32, 495–502

Wilcox, R. R. (2005). A comparison of six smoothers when there aremultiple predictors. Statistical Methodology, 2, 49–57.

Wilcox, R. R. (2005). Within by Within ANOVA based on medians.Journal of Modern Applied Statistical Methods, 4, 2–10.

Wilcox, R. R. & Earleywine, M. (2005). Inferences about regressioninteractions via a robust smoother with an application to cannabisproblems. Journal of Modern Applied Statistical Methods, 4, 53–62.

Wilcox, R. R. (2005). Comparing medians: An overview plus new resultson dealing with heavy-tailed distributions. Journal of ExperimentalEducation, 73, 249–263.

Wilcox, R. R. (2005). New methods for comparing groups: Strategiesfor increasing the probability of detecting true differences.Current Directions in Psychology, 14, 272–275

Wilcox, R. R. (2005). Depth and a multivariate generalization of the

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Wilcoxon-Mann-Whitney test. American Journal of Mathematical andManagement Sciences, 25, 343–364

Wilcox, R. R. (2006). Confidence intervals for prediction intervals.Journal of Applied Statistics, 33, 317–326.

Wilcox, R. R. (2006). Testing the hypothesis of a homoscedastic errorterm in simple, nonparametric regression. Educational andPsychological Measurement, 66, 85–92

Wilcox, R. R. & Keselman, H. J. (2006). Detecting heteroscedasticityin a simple regression model via quantile regression slopes.Journal of Statistical Computation and Simulation, 76, 705-712.

Keselman, H. J., Wilcox, R. R., Lix, L. M., Algina, J. & Fradette, B. A. (2007).Adaptive robust estimation and testing. British Journalof Mathematical and Statistical Psychology, 60, 267–294.

Mathiyakom, W., McNitt-Gray, J. L. & Wilcox, R. R. (2006). Lower extremitycontrol and dynamics during backward angular impulse generation in forwardtranslating tasks. Journal of Biomechanics, 39, 990–1000.

Wilcox, R. R. & Keselman, H. J. (2006). A skipped multivariatemeasure of location: One- and two-sample hypothesis testing.In S. Sawilowsky (Ed.) Real Data Analysis (pp. 125-138). Charlotte, NC: IAP.

Wilcox, R. R. (2006). Inferences about the components of a generalizedadditive model. Journal of Modern Applied Statistical Methods, 5,, 309–316.

Wilcox, R. R. (2006). Pairwise comparisons of dependent groups basedon medians. Computational Statistics & Data Analysis, 502933-2941.

Wilcox, R. R. (2006). Comparing medians. Computational Statistics& Data Analysis, 51, 1934-1943.

Wilcox, R. R. (2006). A note on inferences about the median of differencescores. Educational and Psychological Measurement, 66, 624-630.

Wilcox, R. R. (2006). Comparing robust generalized variances and

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comments on efficiency. Statistical Methodology, 3, 211-223.

Wilcox, R. R. (2006). Graphical methods for assessing effect size:Some alternatives to Cohen’s d. Journal of ExperimentalEducation, 74, 353–367

Wilcox, R. R. (2006). ANCOVA: A robust omnibus test based on selecteddesign points. Journal of Modern Applied Statistical Methods, 514-21.

Kowalchuk, R. K, Keselman, H. J., Algina, J. & Wilcox, R. R. (2006).Multiple Comparison Procedures, Trimmed Means and Transformed StatisticsJournal of Modern Applied Statistical Methods, 5, 43-64

Smucker, S., Earleywine, M.& Wilcox, R. R. (2006). Cannabis,Motivation, and Life Satisfaction. Substance Abuse Treatment,Prevention, and Policy, 1: 2.

Wilcox, R. R. (2007). Local measures of association: Estimating thederivative of the regression line. British Journal ofMathematical and Statistical Psychology, 60, 107-117.

Wilcox, R. R. (2006). Some results on comparing the quantiles of dependentgroups. Communications in Statistics-Simulation and Computation35, 893–900. DOI:10.1080/03610910600880260

Wilcox, R. R. (2006). Kolmogorov-Smirnov Test for the One Sample Case.In N. Salkind (Ed.) Encyclopedia of Measurement and Statistics,pp. 512-514,Sage: Thousand Oaks, CA.

Wilcox, R. R. (2006). Kolmogorov Smirnov Test for the Two Sample CaseIn N. Salkind (Ed.) Encyclopedia of Measurement and Statistics,pp. 514-516. Sage: Thousand Oaks, CA.

Wilcox, R. R. (2008). Sample size and power. In A. Nezu & C. Nezu (Eds.) Evidence-basedoutcome research: A practical guide to conducting randomized clinical trialsfor psychosocial interventions. Oxford University Press, pp 123–124.

Wilcox, R. R. (2007). Robust ANCOVA: Some small-sample results whenthere are multiple groups and multiple covariates. Journal ofApplied Statistics, 34, 353–364.

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Wilcox, R. R. & Costa, K. (2009). Quantile regression: On inferencesabout the slopes corresponding to one, two or three quantiles.Journal of Modern and Applied Statistical Methods, 8, 368–375.

Fournier, B., Rupin, N., Bigerelle, M., Najjar, D. & Wilcox, R. R. (2007).Estimating the parameters of a generalized lambda distribution.Computational Statistics & Data Analysis, 51, 2813–2835.

Mathiyakom, W., McNitt-Gray, J.L., & Wilcox, R. (2006).Lower extremity control and dynamics during backward angular impulsegeneration in backward translating tasks, Experimental Brain Research, 169,377–388.

Wilcox, R. R., Sheather, S., Brunner, E. & Schimek, M. (2007).Special Issue on Nonparametric and Robust Methods. ComputationalStatistics & Data Analysis, 51, 5010-5012.

Wilcox, R. R. (2008). Some small-sample properties of some recentlyproposed multivariate outlier detection techniques. Journal ofStatistical Computation and Simulation, 78, 701–712.

Wilcox, R. R. (2007). An omnibus test when using a quantile regressionestimator with multiple predictors. Journal of Modern and AppliedStatistical Methods, 6, 361–366

Keselman, H. J., Wilcox, R. R., Algina, J., Othman, A. R., Fradette, K. (2008).A comparative study of robust tests for spread: Asymmetric trimming strategiesBritish Journal of Mathematical and Statistical Psychology, 61,235–253.

Wilcox, R. R. (2008). Post-hoc analyses in multiple regression based onprediction error. Journal of Applied Statistics, 35, 9–17.

Cribbie, R. A., Wilcox, R. R., Bewell, C, Keselman, H. J. (2007).Tests for treatment group equality when data are nonnormal and heteroscedastic.Journal of Modern and Applied Statistical Methods, 6, 117–132.

Tian, T. & Wilcox, R. R. (2007). A comparison of two rank tests forrepeated measures designs. Journal of Modern and Applied Statistical

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Methods, 6, 331-335

Wilcox, R. R. (2007). On flexible tests of independence and homoscedasticity.Journal of Modern and Applied Statistical Methods, 6, 30–35

Wilcox, R. R. (2008). Constant. In P. J. Lavrakas (Ed.) Encyclopediaof Survey Research. Sage: Thousand Oaks, CA.

Wilcox, R. R. & Tian, T. (2008). Comparing dependent correlations.Journal of General Psychology, 135, 105–112.

Wilcox, R. R. (2008). Robust Methods for Detecting and Describing AssociationsIn J. Osborne (Ed.), Best Practices in Quantitative MethodsSage: Thousand Oaks, CA, pp. 263–282.

Schug, R., Raine, A. & Wilcox, R. R. (2007). Psychophysiological andBehavioral Characteristics of Individuals Comorbid For AntisocialPersonality Disorder and Schizophrenia-Spectrum Personality DisorderBritish Journal of Psychiatry, 191, 408–414.

Wilcox, R. R. (2008). Robust principal components: A generalizedvariance perspective Behavioral Research Methods, 40, 102–108.

Dawson, M. E., Rissling, A. J., Schell, A. M. & Wilcox, R. (2007).Under what conditions can human affective conditioning occur withoutcontingency awareness? Test of the evaluative conditioning paradigm.Emotion, 7, 755–766.

Keselman, H. J., Wilcox, R. R., Lix, L. M., Algina, J. & Fradette, K. (2007).Adaptive robust estimation and testing. British Journal ofMathemtical and Statistical Psychology, 60, 267–293.DOI: 10.1348/000711005X63755

Wilcox, R. R. (2008). Quantile regression: A simplified approachto a lack-of-fit test. Journal of Data Science, 6, 547–556.

Wilcox, R. R. (2008). Variance. In P. J. Lavrakas (Ed.) Encyclopediaof Survey Research. Sage: Thousand Oaks, CA.

Wilcox, R. R. (2009). Two-by-two ANOVA: Global and graphical comparisons based

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on an extension of the shift function. Journal of Data Science.7, 459–478.

Keselman, H. J., Algina, J., Lix, L. M., Wilcox, R. R. & Deering, K. (2008).A Generally Robust Approach for Testing Hypotheses and Setting ConfidenceIntervals for Effect Sizes. Psychological Methods, 13, 110–129.

Yin, T. S., Othman, A. R. Keselman, H. J. & Wilcox, R. R. (2008).Inferences Based on trimmed means. Proceedingsof the 3rd Regional Conference on Mathematics, Statistics and ApplicationsUniversiti Sains Malaysia, pp 987–993.

Wilcox, R. R. (2009). Robust ANCOVA using a smoother with bootstrapbagging. British Journal of Mathematical and Statistical Psychology62, 427–437.

Wilcox, R. R. (2009). Robust Data Analysis. In R. Millsap &A Maydeu-Olivars (Eds.) Handbook of Quantitative Methods inPsychology. pp. 387–403. Thousand Oaks, CA: Sage

Wilcox, R. R. (2009). Robust multivariate regression when there is heteroscedasticity.Communications in Statistics– Simulation and Computation, 38, 1–13.

Wilcox, R. R. (2009). Nonparametric estimation. In D. A. Belsley andE. J. Kontoghiorghes (Eds) Handbook on Computational Econometrics.New York: Wiley, pp. 153–182.

Wilcox, R. R. (2008). Estimating explanatory power in a simple regressionmodel via smoothers. Journal of Modern and Applied Statistical Methods7, 368–375.

Ng, M. & Wilcox, R. R. (2009). Level robust methods based on theleast squares regression line. Journal of Modern and Applied StatisticalMethods, 8, 384–395.

Wilcox, R. R. (2010). Regression: Comparing predictors and groups of predictorsbased on robust measures of association. Journal of DataScience, 8, 429–441.

Wilcox, R. R. (2010). Comparing robust nonparametric regressionlines via regression depth. Journal of Statistical

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Computation and Simulation, 80, 379–387.

Ng, M. & Wilcox, R. R. (2010). Comparing the slopes of regression lines.British Journal of Mathematical and Statistical Psychology, 63,319–340

Wilcox, R. R. (2010). Measuring and detecting associations:Methods based on robust regression estimators or smoothers that allowcurvature. British Journal of Mathematical and Statistical Psychology63, 379–393.

Wilcox, R. R. (2009). Comparing robust measures of associationestimated via a smoother. Communications in Statistics–Simulation and Computation, 38, 1969–1979.

Wilcox, R. R. (2009). Comparing Pearson correlations: Dealing withheteroscedasticity and non-normality. Communications inStatistics–Simulation and Computation, 38, 2220-2234.

Wilcox, R. R. (2008). A test of independence via quantiles that issensitive to curvature. Journal of Modern and Applied Statistics, 7, 11–20.

Wilcox, R. R. (2011). Comparing two dependent groups: Dealing with missingvalues. Journal of Data Science, 9, 1–13.

Tian, T., James, G., & Wilcox, R. R. (2010). A multivariate adaptivestochastic search method for dimensionality reduction in classification.Annals of Applied Statistics, 4, 340–365.

Wilcox, R. R. (2010) Inferences about the population mean:Empirical likelihood versus bootstrap-t. Journal of Modernand Applied Statistics, 9, 9–14.

Erceg-Hurn, D. M., Wilcox, R. R., & Keselman, H. J. (2013).Robust statistical estimation. In T. Little (Ed.), The OxfordHandbook of Quantitative Methods, Vol. 1, 388–406.New York: Oxford University Press.

Wilcox, R. R. (2011). Comparing the strength of association of two predictors viasmoothers or robust regression estimators. Journal of Modern

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Applied Statistical Methods, 10, 8–18

Victoroff, J., Quota, S., Adelman, J. R., Celinska, M. A., Stern, N.Wilcox, R. & Sapolsky, R. M. (2010). Support for Religio-PoliticalAggression among Teenaged Boys in Gaza: Part I: PsychologicalFindings Aggressive Behavior, 36, 219–231.

Wilcox, R. R. & Tian, T. (2011). Measuring effect size: A robustheteroscedastic approach for two or more groups.Journal of Applied Statistics, 38, 1359–1368.

Tian, T. S., Wilcox, R. R. & James, G. M. (2010). Data Reduction inClassification: A Simulated Annealing Based Projection Method.Statistical Analysis and Data Mining, 3, 319–331.

Wilcox, R. R. (2010). Trimmed means. In N. Salkind (Ed.) Encyclopedia ofResearch Design. Sage: Thousand Oaks, CA.

Victoroff, J., Quota, S., Adelman, J. R., Celinska, M. A., Stern, N.Wilcox, R. & Sapolsky, R. M. (2011). Support for Religio-PoliticalAggression among Teenaged Boys in Gaza: Part II: NeuroendocinologicalFindings Aggressive Behavior, 37, 121–132.

Carlson, M., Wilcox, R., Chou, C.-P., Chang, M., & Yang, F.,Blanchard, J., Marterella, A., Kuo, A. and Clark, F. (2011).Psychometric Properties of Reverse-Scored Items on the CES-D in aSample of Ethnically Diverse Older Adults. Psychological Assessment23, 558–562.

Wilcox, R. R. (2011). Inferences about a probabilistic measure of effectsize when dealing with more than two groups. Journal ofData Science, 9, 471–486.

Cribbie, R. A., Fiksenbaum, L., Keselman, H. J. & Wilcox, R. R. (2012).Effects of nonnormality on test statistics for one-way independent groupsdesigns. British Journal of Mathematical and Statistical Psychology,65, 56–73.

Clark, F., Jackson, J., Carlson, M., Chou,C.-P., Cherry, B. J.,Jordan-Marsh, M., Knight, B. G., Mandel, D. Blanchard, J.,

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Granger, D. A., Wilcox, R. R., Lai, M. Y., White, B., Hay, J.,Lam, C., Marterella, A., & Azen, S. P. (2012). Effectivenessof a lifestyle intervention in promoting the wellbeing of independentlyliving older people: results of the well elderly 2 randomisedcontrolled trial. Journal of Epidemiology and Community Health, 66,782–790. doi:10.1136/jech.2009.099754

Ng, M. & Wilcox, R. R. (2012). Bootstrap methods for comparingindependent regression slopes. British Journal of Mathematical andStatistical Psychology, 65, 282–301.

Ng, M. & Wilcox, R. R. (2010). The small-sample efficiency of somerecently proposed multivariate measures of location.Journal of Modern and Applied Statistical Methods, 9, 28–42.

Ozdemir, A. F. & Wilcox, R. R. (2012). New results on thesmall-sample properties of some robust univariate estimators.Communications in Statistics–Simulation and Computations,41, 1544–1556.

Ozdemir, A. F., Wilcox, R. R. & Yildiztepe, E. (2013). Comparingmeasures of location: Some small-sample results when distributionsdiffer in skewness and kurtosis under heterogeneity of variancesCommunications in Statistics–Simulation and Computations, 42407–424

Tian, T. S., Wilcox, R. R. & James, G. M. (2010). Data reduction inclassification: A simulated annealing based projection method.Statistical Analysis and Data Mining, 3, 319-331.

Wilcox, R. R. & Keselman, H. J. (2012). Modern regression methods thatcan substantially increase power and provide a more accurateunderstanding of associations. European Journal of Personality,26, 165–174. DOI: 10.1002/per.860

Wilcox, R. R. (2012). Nonparametric regression when estimating theprobability of success. Journal of Statistical Theory and Practice,6, 1–9.

Ng, M. & Wilcox, R. R. (2011). A comparison of two-stage procedures for testing

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least squares coefficients under heteroscedasticity. British Journal ofMathematical and Statistical Psychology, 64, 244–258.DOI: 10.1348/000711010X508683

Wilcox, R. R. & Erceg-Hurn, D. (2012). Comparing two dependent groups viaquantiles. Journal of Applied Statistics, 39, 2655–2664.

Othman, A. R., Yin, T. S., Keselman, H., Wilcox, R. R. & Algina, J. (2012). RobustModifications of the Levene and O’Brien Tests for Spread. Journal of ModernApplied Statistical Methods, 11, 54–68

Wilcox, R. R., Carlson, M., Azen, S. & Clark, F. (2013). Avoid lostdiscoveries, due to violations of standard assumptions, by using modernrobust statistical methods. Journal of Clinical Epidemiology, 66, 319–329.

Wilcox, R. R. (2012). Comparing two independentgroups via a quantile generalization of the Wilcoxon–Mann–Whitneytest. Journal of Modern and Applied Statistical Methods, 11, 296–302.

Wilcox, R. R. (2013). A heteroscedastic method for comparing regressionlines at specified design points when using a robustregression estimator. Journal of Data Science, 11, 281–291.available on PUB MED: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3742338

Wilcox, R. R., Erceg-Hurn, D., Clark, F. & Carlson, M. (2014). Comparing twoindependent groups via the lower and upper quantiles. Journal of StatisticalComputation and Simulation, 84, 1543–1551. DOI: 10.1080/00949655.2012.754026

Pernet, C. R., Wilcox, R. & Rousselet, G A. (2013). Robust correlation analyses:a Matlab toolbox for psychology research. Frontiers in Quantitative Psychologyand Measurement. DOI=10.3389/fpsyg.2012.00606https://drive.google.com/a/usc.edu/#folders/0B87FZj4AMIinV2h5WHJlUDY0LVU

Wilcox, R. R., Vigen, C., Clark, F. & Carlson, M (2013). Comparing discretedistributions when the sample space is small. Universitas Psychologica, 12,1583–1595. doi:10.11144/Javeriana.UPSY12-5.cdds

Wilcox, R. R. & Clark, F. (2015). Heteroscedastic global tests that theregression parameters for two or more independent groups are identical.Communications in Statistics–Simulation and Computation, 44, 773–786.DOI: 10.1080/03610918.2013.784986

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Tavakol, M. & Wilcox, R. (2013). Medical education research:the application of robust statistical methods. InternationalJournal of Medical Education, 4, 93–95.DOI: 10.5116/ijme.5181.5fe8

Ma, J. & Wilcox, R. R. (2013). Robust within groups ANOVA: dealing withmissing values. Mathematics and Statistics,1 , 1–4. Horizon Research PublishingDOI: 10.13189/ms.2013.010101

Wilcox, R. R. & Clark, F. (2013). Robust regression estimators whenthere are tied values. Journal of Modern and Applied StatisticalMethods,12, 20–34.

Wilcox, R. R., Granger, D. & Clark, F. (2013). Modern robust statistical methods:Basics with illustrations using psychobiological data. Universal Journal of Psychology, 1,21–31. doi: 10.13189/ujp.2013.010201

Keselman, H. J., Othman, A. R. & Wilcox, R. (2013). Preliminary Testingfor Normality: Is this a Good Practice? Journal of Modern and AppliedStatistical Methods, 12, 2–19.

Wilcox, R. R., Granger, D., Szanton, S. & Clark, F. (2014). Cortisol DiurnalPatterns, Associations with Depressive Symptoms, and the Impact ofIntervention in Older Adults: Results Using Modern Robust Methods Aimed atDealing with Low Power Due to Violations of Standard AssumptionsHormones and Behavior, 65, 219–225.http://dx.doi.org/10.1016/j.yhbeh.2014.01.005PubMed Central http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3960304

Keselman, H. J., Othman, A. R. & Wilcox, R. (2014). Testing for normality inthe Multi-group Problem: Is this a Good Practice? Clinical Dermatology, 229–43.

Wilcox, R. R. & Clark, F. (2014). Comparing robust regression linesassociated with two dependent groups when there is heteroscedasticity.Computational Statistics, 29, 1175–1186.http://link.springer.com/article/10.1007/s00180-014-0485-2

Wilcox, R. R., Granger, D. A., Szanton, S. & Clark, F. (2014).

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Diurnal Patterns and Associations Among Salivary Cortisol,DHEA and Alpha-Amylase in Older Adults. Physiological Behavior, 129, 11–16.PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4041594

Wilcox, R. R. & Hayes, T. (2016). Within groups ANOVA when using a robustmultivariate measure of location. Journal of Modern and AppliedStatistical Methods, 15 Iss. 2 , Article 6.DOI: 10.22237/jmasm/1478001840

Wilcox, R. R. & Clark, F. (2015). Robust multiple comparisons based on combinedprobabilities from independent tests. Journal of Data Science, 13, 43–52.10.6339/JDS.2015.13(1). 140308

Wilcox, R. R. (2014). Gaining a deeper and more accurate understanding of datavia modern robust statistical techniques. Journal of Psychology &Clinical Psychiatry, 1(2): 00012http://medcraveonline.com/JPCPY/JPCPY-01-00012.pdf

Wilcox, R. R. (2014). Modern robust statistical methods can providesubstantially higher power and a deeper understanding of data.Annals of Phytomedicine, 3, 25-30.

Wilcox, R. R. (2015). Estimating the strength of an association basedon a robust smoother. Journal of Modern and AppliedStatistical Methods, 14, 2-11.

Wilcox, R. (2015). Within groups ANCOVA: multiple comparisonsat specified design points using a robust measure of location when thereis curvature. Journal of Statistical Computation and Simulation, 86, 3236–3246.http://dx.doi.org/10.1080/00949655.2014.962536

Othman, A. R., Keselman, H. J. & Wilcox, R. R. (2015). Assessing normality:Applications in multi-group designs. Malaysian Journal ofMathematical Sciences, 9, 53–65.

Wilcox, R. R (2015). Inferences about the skipped correlation coefficient:dealing with heteroscedasticity and non-normality. Journal of Modernand Applied Statistical Methods, 14, 2–8.http://digitalcommons.wayne.edu/jmasm/vol14/iss2/4

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Wilcox, R. R. (2017). Global comparisons of medians and other quantilesin a one-way design when there are tied values. CommunicationsStatistics–Simulation and Computation, 46, 3010–3019.DOI:10.1080/03610918.2015.1071388http://www.tandfonline.com/eprint/P7Wx2dvg4EWVbMM7q3ic/full

Wilcox, R. R. (2016). ANCOVA: A global test based on a robust measure oflocation or quantiles when there is curvature. Journal of Modernand Applied Statistical Methods, 15, 12–31.http://digitalcommons.wayne.edu/jmasm/vol15/iss1/3

Wilcox, R. R. (2015). Comparing the variances of two dependent variables.Journal of Statistical Distributions and Applications, 2:7.DOI: 10.1186/s40488-015-0030-z URL: http://www.jsdajournal.com/content/2/1/7

Wilcox, R. R. & Ma, J. (2015). Heteroscedastic methods for performing allpairwise comparisons of regression lines associated with J independentgroups. Methodology, 11, 110–115. DOI: 10.1027/1614-2241/a000097

Wilcox, R. R., Granger, D. A., Szanton, S. & Clark, F. (2015).In older adults, intervention impacts the association between the cortisol awakeningresponse and three measures of wellbeing: meaningful activities, life satisfactionand perceived control. Jacobs Journal of Biomarkers, 1(2): 011.

Russell, I. M., Raina, S., Requejo, P. S., Wilcox, R., Mulroy, S. & McNitt-Gray, J. L. (2015).Modifications in wheelchair propulsion technique with speed.Frontiers in Bioengineering and Biotechnology, 26http://dx.doi.org/10.3389/fbioe.2015.00171Reproduced in P. Requejo and J. McNitt-Gray (Eds.)Wheeled Mobility Biomechanics. Frontiers Research Topics. DOI: 10.3389/978-2-88919-938-9

Wilcox, R. R. (2016). Comparisons of two quantile regression smoothers.Journal of Modern and Applied Statistical Methods, 15, 62–77.http://digitalcommons.wayne.edu/jmasm/vol15/iss1/5

Keselman, H. J., Othman, A & Wilcox, R. R. (2016). Generalized Linear ModelAnalyses for treatment group equality when data are non-normalJournal of Modern and Applied Statistical Methods,15, 32–61.

Wilcox, R. R. (2016). ANCOVA: A heteroscedastic global test when there

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is curvature and two covariates. Computational Statistics, 31, 1593–1606.doi:10.1007/s00180-015-0640-4

Estevez-Perez, G., Andrade, J. M. & Wilcox, R. R. (2016). A bootstrap approach to comparethe slopes of two calibrations when few standards are available Analytical Chemistry, 882289–2295.

Zaferiou, A.M., Wilcox, R.R., McNitt-Gray, J.L. (2016). Modification of impulse generationduring pique turns with increased rotational demands. Human Movement Science, 47220–230. doi:10.1016/j.humov.2016.03.012

Peterson, T., Wilcox, R. & McNitt-Gray, J. (2016). Angular Impulse and Balance Regulationduring the Golf Swing Journal of Applied Biomechanics, 32, 342–349.http://dx.doi.org/10.1123/jab.2015-0131

Wilcox, R. R. & Serang, S. (2016). Hypothesis Testing, p values, confidence intervals,measures of effect size and Bayesian methods in light of modern robust techniques.Educational and Psychological Measurementdoi:10.1177/0013164416667983

Wilcox, R. R. (2017). Robust ANCOVA: Confidence intervals that have some specifiedsimultaneous probability coverage when there is curvature and two covariatesJournal of Modern and Applied Statistical Methods, 16, 3–19.http://digitalcommons.wayne.edu/jmasm/

Wilcox, R. R. (2016). Comparing dependent robust correlations. BritishJournal of Mathematical and Statistical Psychology, 69, 215–224.DOI: 10.1111/bmsp.12069

Zaferiou, A. M., Wilcox, R. R. & McNitt-Gray, J. L. (2016). Whole-body balance regulationduring the turn phase of piqu and pirouette turns with varied rotational demandsMedical Problems of Performing Artists, 31, 96–103.

Wilcox, R. R. (2017). Linear regression: Heteroscedastic confidence bands for thetypical value of Y, given X, having some specified simultaneous probability coverage.Journal of Applied Statistics, 44, 2564–2574. DOI: 10.1080/02664763.2016.1257591.http://dx.doi.org/10.1080/02664763.2016.1257591http://www.tandfonline.com/eprint/qrTxPrM8WCttCySBZ2Zz/full

Wilcox, R. (2017). The regression smoother lowess: A confidence band that allows

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heteroscedasticity and has some specified simultaneous probability coverage.Journal of Modern and Applied Statistical Methods,16, 29–38. doi: 10.22237/jmasm/.

Wilcox, R. R. (2017). Robust Testing Procedures. Wiley StatsRef: Statistics Reference OnlineDOI: http://dx.doi.org/10.1002/9781118445112.stat06356.pub2

Wilcox, R. R. (2018). Robust regression: an inferential method for determining whichindependent variables are most important. Journal of Applied Statistics, 45, 100–111http://www.tandfonline.com/eprint/6sZMRAfGqmxErZRTpmRj/fullhttp://dx.doi.org/10.1080/02664763.2016.1268105

Wilcox, R. R. (2017). Winsorized Robust Measures. Wiley StatsRef: Statistics Reference Onlinehttp://onlinelibrary.wiley.com/doi/10.1002/9781118445112.stat06339.pub2/full

Zaferiou, A. M., Flashner, H., Wilcox, R. R. & McNitt-Gray, J. L. (2017).Lower Extremity Control during Turns Initiated With and Without HipExternal Rotation Journal of Biomechanics.DOI: http://dx.doi.org/10.1016/j.jbiomech.2016.12.017

Wilcox, R. R. (2017). Linear contrasts based on an extension of the Wilcoxon–Mann–Whitneyapproach. International Journal of Statistics and Probability, 6, No. 3doi:10.5539/ijsp.v6n3p198 URL: https://doi.org/10.5539/ijsp.v6n3p198

Wilcox, R. R. (2017). Robust ANCOVA: heteroscedastic confidence bands thathave some specified simultaneous probability coverage. Journal of Data Science, 15,313-328.

Ozdemir, A. F., Wilcox, R. R. & Yildiztepe, E. (2018). Comparing J independent groups with amethod based on trimmed means. Communications in Statistics–Simulation and Computation, 47, 852–863. DOI: 10.1080/03610918.2017.1295152

Wilcox, R. R. (2017). The running interval smoother: a confidence band having somespecified simultaneous probability coverage. International Journal of Statistics:Advances in Theory and Applications, 1, 21–43.

Wilcox, R. R. (2017). New statistical methods would let researchers deal with data in better,more robust ways. The Conversation.http://theconversation.com/new-statistical-methods-would-let-researchers-deal-with-data-in-better-more-robust-ways-67981

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Wilcox, R. R. (2018). An inferential method for determining which of two independentvariables is most important when there is curvature. Journal of Modern and AppliedStatistical Methods, 17 (1), eP2588.doi: 10.22237/jmasm/1525132920

Rousselet, G. A., Pernet, C. R. & Wilcox, R. R. (2017). Beyond differences in means:robust graphical methods to compare two groups in neuroscience.European Journal of Neuroscience DOI: 10.1111/ejn.13610

Rousselet, G.A. & Wilcox, R.R. (2016) rogme: Robust graphical methods forgroup comparisons. R package version 0.1.0.9000.https://github.com/GRousselet/rogme

Field, A. & Wilcox, R. (2017). Robust statistical methods: a primer for clinical psychologyand experimental psychopathology researchers. Behaviour Research and Therapy, 98, 19–38.Preprint available at https://osf.io/fbj3z/

Wilcox, R. R. & Rousselet, G. A. (2018). A guide to robust statistical methods in neuroscience.Current Protocols in Neuroscience, 82, Issue 1, 8.42.1-8.42.30.DOI: 10.1002/cpns.41

Wilcox, R. R. (2018). Robust ANCOVA, curvature and the curse of dimensionality.Journal of Modern Applied Statistical Methods, 17 (2),eP2682. doi: 10.22237/jmasm/1551906370

Multach, M. & Wilcox, R. R. (2017). Some results on a Wilcoxon–Mann–Whitneytype measure of interaction. Advances in Social Sciences Research Journal, 4,No. 20. DOI: http://dx.doi.org/10.14738/assrj.420.2017

Wilcox, R. R. (2018). A robust nonparametric measure of effect size based onan analog of Cohen’s d, plus inferences about the median of the typical difference.Journal of Modern and Applied Statistical Methods, 17 (2),eP2726. doi: 10.22237/jmasm/1551905677

Levy, B., Gable, S., Tsoy, E. , Haspel, N., Wadler, B., Wadler B., Wilcox, R., Hess, C. Hogan, J.Driscoll, D. & Hashmi, A. (2017). Machine Learning Detection of Cognitive Impairment inPrimary Care. Alzheimer’s Disease & Dementia, 1. 38–46.

Wilcox, R. R. (2017). Analogs of the Wilcoxon–Mann–Whitney test when there is a covariate.

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International Journal of Clinical Biostatistics and Biometrics, 3, issue 1, 1–6.DOI: 10.23937/2469-5831/1510015

Wilcox, R. R. (2018). Inferences based on robust regression estimators whenthere is multicollinearity. Advances in Social Sciences Research Journal5(5), 229–238. DoI:10.14738/assrj.55.4492.

Wilcox, R. R., Peterson, T. J. & McNitt-Gray, J. (2018). Data analyses when sample sizes aresmall: Modern advances and insights for dealing with outliers, skewed distributionsand heteroscedasticity Journal of Applied Biomechanicshttps://doi.org/10.1123/jab.2017-0269

Wilcox, R. R., Rousselet, G. A. & Pernet, C. R. (2018). Improved methods for makinginferences about multiple skipped correlations. Journal of StatisticalComputation and Simulation, 88, 3116–3131DOI: 10.1080/00949655.2018.1501051.

Wilcox, R. R. (2019). Multicollinearity and Ridge Regression: Results on Type IErrors, Power and Heteroscedasticity. Journal of Applied Statistics, 46, 946–957.DOI: 10.1080/02664763.2018.1526891

Ramos, C. D., Ramey, M., Wilcox, R. R. & McNitt-Gray, J. L. (2018). Generation of linearimpulse during the takeoff of the long jump. Journal of Applied Biomechanics 1-23.Doi: 10.1123/jab.2017-0249. [Epub ahead of print]

Wilcox, R. (2018). Logistic regression: An inferential method for identifyingthe best predictors. Journal of Modern Applied Statistical Methods, 17 (2),eP3061. doi: 10.22237/jmasm/1551906905

Wilcox, R. R. (2019). Robust regression: Testing global hypotheses about theslopes when there is multicollinearity or heteroscedasticity.British Journal of Mathematical and Statistical Psychology, 72, 355–369.DOI:10.1111/bmsp.12152.

Wilcox, R. R. (in press). Bivariate Analogs of the Wilcoxon–Mann–Whitney testand the Patel–Hoel Method for Interactions.Journal of Modern Applied Statistical Methods

Salminen, L.E., Wilcox, R.R., Zhu, A.H, Riedel, B.C., Ching, C.R.K., Rashid, F.,Thomopoulos, S.I., Saremi, A., Harrison, M.B., Ragothaman, A., Knight, V.,

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Boyle, C.P., Medland, S.E., Thompson, P.M., Jahanshad, N. (In press).Altered cortical brain structure and increased risk for disease seen decadesafter perinatal exposure to maternal smoking: A study of 9,000 adults inthe UK Biobank. Cerebral Cortex.https://doi.org/10.1093/cercor/bhz060

Mair, P. & Wilcox, R. (2019). Robust Statistical Methods in R Using the WRS2 PackageBehavior Research Methods doi.org/10.3758/s13428-019-01246-w

Rousselet, G. A & Wilcox, R. R. (in press). Reaction times and other skewed distributions:problems with the mean and the median. Meta-Psychology

Wilcox, R. R. (in press). Regression when there are two covariates: some practical reasons forconsidering quantile grids. Journal of Modern Applied Statistical Methods

Wilcox, R. R. (in press). Inferences about the probability of success,given the value of a covariate, using a nonparametric smoother.Journal of Modern Applied Statistical Methods

Wilcox, R. R. (in press). A note on inferences about the probability of success.Journal of Modern Applied Statistical Methods

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