bootstrap methods: recent advances and new...

Bootstrap Methods: RecentBootstrap Methods: RecentAdvances and New ApplicationsAdvances and New Applications

2007 ICTP Lectures2007 ICTP LecturesAnestis Anestis AntoniadisAntoniadis

UJF, LJK LabUJF, LJK LabTrieste October, 2007Trieste October, 2007

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Bootstrap TopicsBootstrap Topics• Introduction to Bootstrap

• Wide Variety of Applications

• Confidence regions and hypothesis tests

• Examples where bootstrap is not consistent and remedies:(1) infinite variance case for a population mean and(2) extreme values

• Available Software

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IntroductionIntroduction

• The bootstrap is a general method for doingstatistical analysis without making strongparametric assumptions.

• Efron’s nonparametric bootstrap, resamplesthe original data.

• It was originally designed to estimate bias andstandard errors for statistical estimates muchlike the jackknife.

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Introduction (continued)Introduction (continued)

• The bootstrap is similar to earliertechniques which are also calledresampling methods:– (1) jackknife,– (2) cross-validation,– (3) delta method,– (4) permutation methods, and– (5) subsampling..

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Introduction (continued)Introduction (continued)

The technique was extended, modified and

refined to handle a wide variety of problems

including:– (1) confidence intervals and hypothesis tests,

– (2) linear and nonlinear regression,

– (3) time series analysis and other problems

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Introduction (continued)Introduction (continued)

Definition of Efron’s nonparametric bootstrap.Given a sample of n independent identicallydistributed (i.i.d.) observations X1, X2, …, Xn froma distribution F and a parameter θ of thedistribution F with a real valued estimatorθ(X1, X2, …, Xn ), the bootstrap estimates the

accuracy of the estimator by replacing F with Fn,the empirical distribution, where Fn placesprobability mass 1/n at each observation Xi.

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Introduction (continued)Introduction (continued)

• Let X1*, X2

*, …, Xn* be a bootstrap sample, that

is a sample of size n taken with replacementfrom Fn .

• The bootstrap, estimates the variance of

θ(X1, X2, …, Xn ) by computing orapproximating the variance of

θ* = θ(X1*, X2

*, …, Xn* ).

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Schematic of Bootstrap ProcessSchematic of Bootstrap Process

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Introduction (continued)Introduction (continued)

• Statistical Functionals - A functional is amapping that takes functions into realnumbers.

• Parameters of a distribution can usually beexpressed as functionals of the populationdistribution.

• Often the standard estimate of aparameter is the same functional appliedto the empirical distribution.

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Introduction (continued)Introduction (continued)

• Statistical Functionals and the bootstrap.• A parameter θ is a functional T(F) where T

denotes the functional and F is apopulation distribution.

• An estimator of θ is θh = T(Fn) where Fn isthe empirical distribution function.

• Many statistical problems involveproperties of the distribution of θ - θh , itsmean (bias of θh ), variance, median etc.

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Introduction (continued)Introduction (continued)• Bootstrap idea: Cannot determine the distribution of

θ - θh but through the bootstrap we can determine, orapproximate through Monte Carlo, the distribution ofθh - θ*, where θ* = T(Fn

*) and Fn* is the empirical

distribution for a bootstrap sample X1*, X2

*,…,Xn*

(θ* is a bootstrap estimate of θ).• Based on k bootstrap samples the Monte Carlo

approximation to the distribution of θh - θ* is used toestimate bias, variance etc. for θh .

• In bootstrapping θh substitutes for θ and θ* substitutes forθh . Called the bootstrap principle.

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Introduction (continued)Introduction (continued)

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Introduction (continued)Introduction (continued)

• Basic Theory: Mathematical results show thatbootstrap estimates are consistent in particularcases.

• Basic Idea: Empirical distributions behave inlarge samples like population distributions.Glivenko-Cantelli Theorem tells us this.

• The smoothness condition is needed to transferconsistency to functionals of Fn, such as theestimate of the parameter θ.

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Wide Variety of ApplicationsWide Variety of Applications

• Efron and others recognized that throughthe power of fast computing the MonteCarlo approximation could be used toextend the bootstrap to many differentstatistical problems .

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Wide Variety of ApplicationsWide Variety of Applications(continued)(continued)

• It can estimate process capability indices fornon-Gaussian data.

• It is used to adjust p-values in a variety ofmultiple comparison situations.

• It can be extended to problems involvingdependent data including multivariate, spatialand time series data and in sampling fromfinite populations.

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Wide Variety of ApplicationsWide Variety of Applications(continued)(continued)

• It also has been applied to problems involvingmissing data.

• In many cases, the theory justifying the use ofbootstrap (e.g. consistency theorems) has beenextended to these non i.i.d. settings.

• In other cases, the bootstrap has been modifiedto “make it work.” The general case ofconfidence interval estimation is a notableexample.

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Confidence regions and hypothesisConfidence regions and hypothesisteststests

• The percentile method and other bootstrapvariations may require 1000 or morebootstrap replications to be very useful.

• The percentile method only works underspecial conditions.

• Bias correction and other adjustments aresometimes needed to make the bootstrap“accurate” and “correct” when the samplesize n is small or moderate.

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Confidence regions and hypothesisConfidence regions and hypothesistests (continued)tests (continued)

• Confidence intervals are accurate or nearlyexact when the stated confidence level for theintervals is approximately the long run probabilitythat the random interval contains the “true” valueof the parameter.

• Accurate confidence intervals are said to becorrect if they are approximately the shortestlength confidence intervals possible for the givenconfidence level.

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Examples where the bootstrap failsExamples where the bootstrap fails

• Athreya (1987) shows that the bootstrapestimate of the sample mean isinconsistent when the populationdistribution has an infinite variance.

• Angus (1993) provides similarinconsistency results for the maximumand minimum of a sequence ofindependent identically distributedobservations.

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Bootstrap RemediesBootstrap Remedies

• In the past decade many of the problemswhere the bootstrap is inconsistentremedies have been found by researchersto give good modified bootstrap solutionsthat are consistent.

• For both problems describe thus far asimple procedure called the m-out-nbootstrap has been shown to lead toconsistent estimates .

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The The m-out-of-n m-out-of-n BootstrapBootstrap

• This idea was proposed by Bickel and Ren (1996) forhandling doubly censored data.

• Instead of sampling n times with replacement from asample of size n they suggest to do it only m timeswhere m is much less than n.

• To get the consistency results both m and n need to getlarge but at different rates. We need m=o(n). That ism/n→0 as m and n both → ∞.

• This method leads to consistent bootstrap estimates inmany cases where the ordinary bootstrap has problems,particularly (1) mean with infinite variance and (2)extreme value distributions.

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Available SoftwareAvailable Software• Resampling Stats from Resampling Stats Inc.

(provides basic bootstrap tools in easy to usesoftware and is good as an elementary teaching tool).

• SPlus from Insightful Corporation ( good foradvanced bootstrap techniques such as BCa, easy touse in new Windows based version). The currentmodule Resample is what I use in my bootstrap classat statistics.com.

• S functions provided by Tibshirani (see Appendix inEfron and Tibshirani text or visit Rob Tibshirani’s website http:/www.stat-stanford.edu/~tibs)

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Available Software (continued)Available Software (continued)

• Stata has a bootstrap algorithm available thatsome users rave about.

• Mathworks and other examples (see SusanHolmes web page):

http:/www-stat.stanford.edu/~susan) or contact herby email

• SAS macros are available and Proc MULTTESTdoes bootstrap sampling.

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References on confidence intervalsReferences on confidence intervalsand hypothesis testsand hypothesis tests

(1) Chernick, M.R. (1999). Bootstrap Methods: APractitioner’s Guide. Wiley, New York.(2) Chernick, M.R. (2007). Bootstrap Methods: AGuide for Practitioners and Researchers, 2nd

Edition. Wiley, New York.(3) Hall, P. (1992). The Bootstrap andEdgeworth Expansion. Springer-Verlag, NewYork.(4) Efron, B. (1982) The Jackknife, the Bootstrapand Other Resampling Plans. Society forIndustrial and Applied Mathematics CBMS-NSFRegional Conference Series 38, Philadelphia.

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(5) Carpenter, J. and Bithell, J. (2000).Bootstrap confidence intervals: when, which,what? A practical guide for medical statisticians.Statistics in Medicine 19, 1141-1164.(6) Bahadur, R.R. and Savage, L.J. (1956). Thenonexistence of certain statistical procedures innonparametric problems. Annals ofMathematical Statistics 27, 1115-1122.(7) Ewens, W.J. and Grant, G.R. (2001).Statistical Methods in Bioinformatics AnIntroduction.

References on confidence intervals andReferences on confidence intervals andhypothesis tests (continued)hypothesis tests (continued)

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References on when bootstrap failsReferences on when bootstrap fails(1) Angus, J. E. (1993). Asymptotic theory forbootstrapping the extremes. Communs. Statist.Theory and Methods 22, 15-30.(2) Athreya, K. B. (1987). Bootstrap estimationof the mean in the infinite variance case. Ann.Statist. 15, 724-731.(3) Bickel, P. J. and Freedman, D. A. (1981).Some asymptotic theory for the bootstrap. Ann.Statist. 9, 1196-1217.

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References on when bootstrap failsReferences on when bootstrap fails(continued)(continued)

(4) Chernick, M.R. (1999). Bootstrap Methods: APractitioner’s Guide. Wiley, New York.

(5) Chernick, M.R. (2007). Bootstrap Methods: AGuide for Practitioners and Researchers, 2nd

Edition. Wiley, New York.

(6) Cochran, W. (1977). Sampling Techniques.3rd ed., Wiley, New York

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References on when bootstrap failsReferences on when bootstrap fails(continued)(continued)

(7) Knight, K. (1989). On the bootstrap of thesample mean in the infinite variance case. Ann.Statist. 17, 1168-1175.

(8) LePage, R., and Billard, L. (editors). (1992).Exploring the Limit of Bootstrap. Wiley, NewYork.(9) Mammen, E. (1992). When Does theBootstrap Work? Asymptotic Results andSimulations Springer-Verlag, Heidelberg.

(10) Singh, K. (1981). On the asymptoticaccuracy of Efron’s bootstrap. Ann. Statist. 9,1187-1195.

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