Asymptotic Methods in Probability and Statistics with Applications

N. Balakrishnan LA. Ibragimov V.B. Nevzorov Editors

Birkhäuser Boston • Basel • Berlin

Contents

Preface Contributors

PART I: PROBABILITY DISTRIBUTIONS

1 Positive Linnik and Discrete Linnik Distributions Gerd Christoph and Karina Schreiber

1.1 Different Kinds of Linnik's Distributions 3 1.2 Self-deomposability and Discrete Self-decomposability 1.3 Scaling of Positive and Discrete Linnik Laws 8 1.4 Strictly Stahle and Discrete Stahle Distributions as

Limit Laws 9 1.5 Asymptotic Expansions 11

References 15

2 On Finite-Dimensional Archimedean Copulas S. V. Malov

2.1 Introduction 19 2.2 Statements of Main Results 22 2.3 Proofs 25 2.4 Some Examples 30

References 34

PART IL CHARACTERIZATIONS OF DISTRIBUTIONS

3 Characterization and Stability Problems for Finite Quadratic Forms G. Christoph, Yu. Prohorov, and V. Ulyanov

3.1 Introduction 39 3.2 Notations and Main Results 40

v

vi

3.3 Auxiliary Results 43 3.4 Proofs of Theorems 47

References 49

4 A Characterization of Gaussian Distributions by Signs of Even Cumulants L. B. Klebanov and G. J. Szekely

4.1 A Conjecture and Main Theorem 51 4.2 An Example 53

References 53

5 On a Class of Pseudo-Isotropic Distributions A. A. Zinger

5.1 Introduction 55 5.2 The Main Results 56 5.3 Proofs 58

References 61

PART III: PROBABILITIES AND MEASURES IN

HlGH-DlMENSIONAL STRUCTURES

6 Time Reversal of Diffusion Processes in Hilbert Spaces and Manifolds Ya. Belopolskaya

6.1 Diffusion in Hilbert Space 65 6.1.1 Duality of time inhomogeneous

diffusion processes 69 6.2 Diffusion in Hilbert Manifold 72

References 79

7 Localization of Marjorizing Measures Bettina Bühler, Wenbo V. Li, and Werner Linde

7.1 Introduction 81 7.2 Partitions and Weights 83 7.3 Simple Properties of 6 $ ( T ) 84 7.4 Talagrand's Partitioning Scheme 87 7.5 Majorizing Measures 88 7.6 Approximation Properties 89 7.7 Gaussian Processes 93 7.8 Examples 96

References 99

Contents vii

8 Multidimensional Hungarian Construction for Vectors with Almost Gaussian Smooth Distributions 101 F. Götze and A. Yu. Zaitsev

8.1 Introduction 101 8.2 The Main Result 106 8.3 Proofof Theorem 8.2.1 112 8.4 Proof of Theorems 8.1.1-8.1.4 123

References 131

9 On the Existence of Weak Solutions for Stochastic Differential Equations With Driving L2-Valued Measures 133 V. A. Lebedev

9.1 Basic Properties of cr-Finite L^-Valued Random Measures 133

9.2 Formulation and Proof of the Main Result 135 References 141

10 Tightness of Stochastic Families Arising From Randomization Procedures 143 Mikhail Lifshits and Michel Weber

10.1 Introduction 143 10.2 Sufficient Condition of Tightness in C[0,1] 145 10.3 Continuous Generalization 146 10.4 An Example of Non-Tightness in C[0,1] 147 10.5 Sufficient Condition for Tightness in U>[0,1] 149 10.6 Indicator Functions 151 10.7 An Example of Non-Tightness in Lp, p € [1,2) 155

References 158

11 Long-Time Behavior of Multi-Particle Markovian Models 161 A. D. Manita

11.1 Introduction 161 11.2 Convergence Time to Equilibrium 162 11.3 Multi-Particle Markov Chains 163 11.4 H and S-Classes of One-Particle Chains 165 11.5 Minimal CTE for Multi-Particle Chains 167 11.6 Proofs 168

References 176

viii

12 Applications of Infinite-Dimensional Gaussian Integrals 177 A. M. Nikulin

References 187

13 On Maximum of Gaussian Non-Centered Fields Indexed on Smooth Manifolds 189 Vladimir Piterbarg and Sinisha Stamatovich

13.1 Introduction 189 13.2 Definitions, Auxiliary Results, Main Results 190 13.3 Proofs 194

References 203

14 Typical Distributions: Infinite-Dimensional Approaches 205 A. V. Sudakov, V. N. Sudakov, and H. v. Weizsäcker

14.1 Results 205 References 211

PART IV: W E A K AND STRONG LIMIT THEOREMS

15 A Local Limit Theorem for Stationary Processes in the Domain of Attraction of a Normal Distribution 215 Jon Aaronson and Manfred Denker

15.1 Introduction 215 15.2 Gibbs-Markov Processes and Functionals 216 15.3 Local Limit Theorems 218

References 223

16 On the Maximal Excursion Over Increasing Runs 225 Andrei Frolov, Alexander Martikainen, and Josef Steinebach

16.1 Introduction 225 16.2 Results 230 16.3 Proofs 232

References 240

17 Almost Sure Behaviour of Partial Maxima Sequences of Some m-Dependent Stationary Sequences 243 George Haiman and Lhassan Habach

17.1 Introduction 243 17.2 Proofof Theorem 17.1.2 245

References 249

Contents ix

18 On a Strong Limit Theorem for Sums of Independent Random Variables 251 Valentin V. Petrov

18.1 Introduction and Results 251 18.2 Proofs 253

References 256

PART V: LARGE DEVIATION PROBABILITIES

19 Development of Linnik's Work in His Investigation of the Probabilities of Large Deviation 259 A. Aleskeviciene, V. Statulevicius, and K. Padvelskis

19.1 Reminiscences on Yu. V. Linnik (V. Statulevicius) 259 19.2 Theorems of Large Deviations of Sums of Random

Variables Related to a Markov Chain 260 19.3 Non-Gaussian Approximation 272

References 274

20 Lower Bounds on Large Deviation Probabilities for Sums of Independent Random Variables 277 S. V. Nagaev

20.1 Introduction. Statement of Results 277 20.2 Auxiliary Results 283 20.3 Proof of Theorem 20.1.1 286 20.4 Proofof Theorem 20.1.2 291

References 294

PART VI: EMPIRICAL PROCESSES, ORDER STATISTICS, AND RECORDS

21 Characterization of Geometrie Distribution Through Weak Records 299 Fazil A. Aliev

21.1 Introduction 299 21.2 Characterization Theorem 300

References 306

22 Asymptotic Distributions of Statistics Based on Order Statistics and Record Values and Invariant Confidence Intervals 309 Ismihan G. Bairamov, Omer L. Gebizlioglu, and Mehmet F. Kaya

X

22.1 Introduction 309 22.2 The Main Results 312

References 319

23 Record Values in Archimedean Copula Processes 321 N. Balakrishnan, L. N. Nevzorova, and V. B. Nevzorov

23.1 Introduction 321 23.2 Main Results 323 23.3 Sketch of Proof 327

References 329

24 Functional CLT and LIL for Induced Order Statistics 333 Yu. Davydov and V. Egorov

24.1 Introduction 333 24.2 Notation 335 24.3 Functional Central Limit Theorem 335 24.4 Strassen Balls 339 24.5 Law of the Iterated Logarithm 343 24.6 Applications 345

References 347

25 Notes on the KMT Brownian Bridge Approximation to the Uniform Empirical Process 351 David M. Mason

25.1 Introduction 351 25.2 Proof of the KMT Quantile Inequality 355 25.3 The Diadic Scheme 360 25.4 Some Combinatorics 363

References 368

26 Inter-Record Times in Poisson Paced Fa Models 371 H. N. Nagaraja and G. Hofmann

26.1 Introduction 371 26.2 Exact Distributions 372 26.3 Asymptotic Distributions 374

References 381

PART VIL ESTIMATION OF PARAMETERS AND HYPOTHESES TESTING

27 Goodness-of-Fit Tests for the Generalized Additive Risk Models 385 Vilijandas B. Bagdonavicius and Milhail S. Nikulin

27.1 Introduction 385

Contents xi

27.2 Test for the First GAR Model Based on the Estimated Score Function 387

27.3 Tests for the Second GAR Model 391 References 393

28 The Combination of the Sign and Wilcoxon Tests for Symmetry and Their Pitman Efficiency 395 G. Burgio and Ya. Yu. Nikitin

28.1 Introduction 395 28.2 Asymptotic Distribution of the Statistic Gn 397 28.3 Pitman Efficiency of the Proposed Statistic 398 28.4 Basic Inequality for the Pitman Power 402 28.5 Pitman Power for Gn 403 28.6 Conditions of Pitman Optimality 404

References 406

29 Exponential Approximation of Statistical Experiments 409 A. A. Gushchin and E. Valkeila

29.1 Introduction 409 29.2 Characterization of Exponential Experiments and

Their Convergence 412 29.3 Approximation by Exponential Experiments 415

References 422

30 The Asymptotic Distribution of a Sequential Estimator for the Parameter in an AR(1) Model with Stable Errors 425 Joop Mijnheer

30.1 Introduction 425 30.2 Non-Sequential Estimation 426 30.3 Sequential Estimation 431

References 433

31 Estimation Based on the Empirical Characteristic Function 435 Bruno Remillard and Radu Theodorescu

31.1 Introduction 435 31.2 Tailweight Behavior 436 31.3 Parameter Estimation 438 31.4 An Illustration 443 31.5 Numerical Results and Estimator Efficiency 446

References 447

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32 Asymptotic Behavior of Approximate Entropy 451 Andrew L. Rukhin

32.1 Introduction and Summary 451 32.2 Modified Definition of Approximate Entropy and

Covariance Matrix for Prequencies 453 32.3 Limiting Distribution of Approximate Entropy 457

References 460

PART VIII: RANDOM WALKS

33 Threshold Phenomena in Random Walks 465 A. V. Nagaev

33.1 Introduction 465 33.2 Threshold Phenomena in the Risk Process 33.3 Auxiliary Statements 469 33.4 Asymptotic Behavior of the Spitzer Series 33.5 The Asymptotic Behavior of M_i 478 33.6 Threshold Properties of the Boundary

Functionals 480 33.7 The Limiting Distribution for S 481

References 484

34 Identifying a Finite Graph by Its Random Walk 487 Heinrich v. Weizsäcker

References 490

PART IX: MISCELLANEA

35 The Comparison of the Edgeworth and Bergström Expansions 493 Vladimir I. Chebotarev and Anatolü Ya. Zolotukhin

35.1 Introduction and Results 493 35.2 Proofof Lemma 35.1.1 497 35.3 Proofof Lemma 35.1.2 500 35.4 Proof of Theorem 35.1.1 505

References 505

36 Recent Progress in Probabilistic Number Theory 507 Jonas Kubilius

468

471

36.1 Results 507

Contents xiii

PART X: APPLICATIONS TO FINANCE

37 On Mean Value of Profit for Option Holder: Cases of a Non-Classical and the Classical Market Models 523 0. V. Rusakov

37.1 Notation and Statements 523 37.2 Models 524 37.3 Results 531

References 533

38 On the Probability Models to Control the Investor Portfolio 535 S. A. Vavilov

38.1 Introduction 535 38.2 Portfolio Consisting of Zero Coupon Bonds:

The First Scheine 537 38.3 Portfolio Consisting of Arbitrary Securities:

The Second Scheme 541 38.4 Continuous Analogue of the Finite-Order

Autoregression 543 38.5 Conclusions 545

References 545

Index 547