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Extreme Value Theory (EVT): Application to Runway Safety. Wang Yao Department of Statistics Rutgers University [email protected] Mentor: Professor Regina Y. Liu. DIMACS -- July 17, 2008. Motivation. Task :. - PowerPoint PPT Presentation
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Wang YaoDepartment of Statistics
Rutgers [email protected]
Mentor: Professor Regina Y. Liu
DIMACS -- July 17, 2008
Extreme Value Theory (EVT):Application to Runway SafetyExtreme Value Theory (EVT):Application to Runway Safety
Motivation Motivation
X
s
Q: How to determine s such that: P(X> s) .0000001=
allow multiple runway usage to ease air traffic congestion!
Cut-off point: Require all landings to be completed before the cut-off point with certain “guarantee”
Task:
(Extremely small!)
*
Difficulty (why Extreme Value Theory)Difficulty (why Extreme Value Theory)
• Extremely small tail probability e.g. p= 0.0000001
• Few or no occurrences (observations) in reality e.g. Even with sample size=2000
2000 0.0000001 0.0002 1
Possible Solution: Extreme Value Theory (EVT)
Difficulty: No observations!
Overview of EVTOverview of EVT
1: 2: : :n n n nX X X
1 2, , , :nX X X Random sample from unknown distr. fun. F
Order statistics
{ ; 1} { 0; 1}, . .n nb n and a n s t :( )
( ),n n nn
n
X bP x G x
a
Fréchet distribution heavy tail
Weibull distribution finite end point, e.g. uniform dist.
: Tail index ↔ Characterizes tail thickness of F
1
( ) ( ) exp( (1 ) ), ,1 0G x G x x x
1
(1 ) 0xx e for
0 :
0 :
0 :
Gumbel distribution in between, e.g. normal dist.
Extreme QuantileExtreme Quantile
1
(1 ( )) log ( ) (1 )t tt F a x b G x x
Take with , then n
tk
k n
( ) ( )nn nF a x b G x ( )F D G
11 1
1 ( ) log ( ) (1 )t t
t t
Y b Y bF Y G
t a t a
t tY a x b Let
1
p n n
k k
knp
x a b
For want to find s.t. ( )F D G px 1 ( )pF x p
ˆ
1ˆˆ ˆ
ˆp n n
k k
knp
x a b
Estimated by
Learning from Some Known DistributionsLearning from Some Known Distributions
• Generate random samples • For p= 0.001, estimate the p-th upper quantile
• Analysis: • Bootstrap Method:
a resampling technique for obtaining
limiting distribution of any estimator
e.g. Normal, Exponential, Chi-square,…
100n
P=0.001 Distribution Estimated True Error
Case A N(0,1) 4.37951 3.09023 1.28928
Case B Exp(1) 5.99578 6.907755 0.911975
Case C Chi-square(3) 14.1312 16.2662 2.135
pxpx
Small sample size Method of moments
P
Real DataReal Data
1k2k
*
underling distribution/model unknown!
px
Task: Applying Bootstrap Method to find a proper k(Bootstrap method: completely nonparametric approach and does not need to know the underlying distribution)
e.g. Landing distance:
** * * *** ** * ***X
• Analyze landing data collected from airport runways
• Apply bootstrap method with proper choice of k
• Determine the suitable cut-off point --
estimate the tail index , and extreme quantile
Remarks:
pxˆ
1ˆˆ ˆ
ˆp n n
k k
knp
x a b
Yet to be completedYet to be completed
Important project with real application.
Well motivated and requires new interesting statistical methodology
I learned some interesting new subjects, e.g. EVT, bootstrap method.
Statistics is a practical field and theoretically challenging.
Questions?
Questions?
Acknowledgment: Thanks to DIMACS REU!