Nonparametric and Semiparametric Models

316 Pages·2004·2.234 MB·English

by  Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich (auth.)

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(continued after index)
Wolfgang Härdle,Marlene Müller,
Stefan Sperlich,Axel Werwatz
Nonparametric
and Semiparametric
Models
123
Wolfgang Härdle Marlene Müller
CASE – Center for Applied Statistics Fraunhofer ITWM
and Economics Gottlieb-Daimler-Straße
Wirtschaftswissenschaftliche Fakultät 67663 Kaiserslautern
Humboldt-Universität zu Berlin Germany
10178 Berlin [email protected]
Germany
[email protected] Stefan Sperlich
Departamento de Economía
Axel Werwatz
Universidad Carlos III de Madrid
DIW Berlin C./Madrid,126
Königin-Luise-Straße 5 28903 Getafe (Madrid)
14195 Berlin Spain
Germany [email protected]
[email protected]
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Mathematics Subjects Classification (2000):62G07,62G08,62G20,62G09,62G10
ISBN 978-3-642-62076-8 ISBN 978-3-642-17146-8 (eBook)
DOI 10.1007/978-3-642-17146-8
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Preface
The concept of smoothing is a central idea in statistics. Its role is to extract
structural elements of variable complexity from patterns of random varia-
tion. The nonparametric smoothing concept is designed to simultaneously
estimate and model the underlying structure. This involves high dimen-
sionalobjects,likedensityfunctions,regressionsurfacesorconditionalquan-
tiles.Suchobjectsaredifficulttoestimatefordatasetswithmixed,highdi-
mensionalandpartiallyunobservablevariables.Thesemiparametricmodel-
ingtechniquecompromisesthetwoaims,flexibilityandsimplicityofstatis-
ticalprocedures,byintroducingpartialparametriccomponents.These(low
dimensional)componentsallowonetomatchstructuralconditionslikefor
examplelinearityinsomevariablesandmaybeusedtomodeltheinfluence
ofdiscretevariables.Theflexibilityofsemiparametricmodelinghasmadeit
awidelyacceptedstatisticaltechnique.
Theaimofthismonographistopresentthestatisticalandmathematical
principlesofsmoothingwithafocusonapplicabletechniques.Thenecessary
mathematicaltreatmentiseasilyunderstandableandawidevarietyofinter-
activesmoothingexamplesaregiven.Thistextisane-book;itisadownload-
ableentity(http://www.i-xplore.de)whichallowsthereadertorecalculate
allargumentsandapplicationswithoutreferencetoaspecificsoftwareplat-
form. This new technique for proliferation of methods and ideas is specifi-
callydesignedforthebeginnerinnonparametricandsemiparametricstatis-
tics.ItisbasedontheXploRequantlettechnology,developedatHumboldt-
Universita¨tzuBerlin.
Thetexthasevolvedoutofthecourses“NonparametricModeling”and
“SemiparametricModeling”,thattheauthorstaughtatHumboldt-Universi-
ta¨tzuBerlin,ENSAEParis,CharlesUniversityPrague,andUniversidadde
Cantabria,Santander.Thebookdividesitselfnaturallyintotwoparts:
VI Preface
• PartI:NonparametricModels
histogram,kerneldensityestimation,nonparametricregression
• PartII:SemiparametricModels
generalized regression, single index models, generalized partial linear
models,additiveandgeneralizedadditivemodels.
The first part (Chapters 2–4) covers the methodological aspects of non-
parametricfunctionestimationforcross-sectionaldata,inparticularkernel
smoothingmethods.Althoughourprimaryfocuswillbeonflexibleregres-
sionmodels,acloselyrelatedtopictoconsiderisnonparametricdensityesti-
mation.Sincemanytechniquesandconceptsfortheestimationofprobability
densityfunctionsarealsorelevantforregressionfunctionestimation,wefirst
considerhistograms(Chapter2)andkerneldensityestimates(Chapter3)in
moredetail.Finally,inChapter4weintroduceseveralmethodsofnonpara-
metrically estimating regression functions. The main part of this chapter is
devotedtokernelregression,butotherapproachessuchassplines,orthogo-
nalseriesandnearestneighbormethodsarealsocovered.
Thefirstpartisintendedforundergraduatestudentsmajoringinmath-
ematics, statistics, econometrics or biometrics. It is assumed that the audi-
encehasabasicknowledgeofmathematics(linearalgebraandanalysis)and
statistics (inference and regression analysis). The material is easy to utilize
sincethee-bookcharacterofthetextallowsmaximumflexibilityinlearning
(andteaching)intensity.
Thesecondpart(Chapters5–9)isdevotedtosemiparametricregression
models,inparticularextensionsoftheparametricgeneralizedlinearmodel.
InChapter5wesummarizethemainideasofthegeneralizedlinearmodel
(GLM).Typicalconceptsarethelogitandprobitmodels.Nonparametricex-
tensionsoftheGLMconsidereitherthelinkfunction(singleindexmodels,
Chapter 6) or the index argument (generalized partial linear models, addi-
tive and generalized additive models, Chapters 7–9). Single index models
focus on the nonparametric error distribution in an underlying latent vari-
ablemodel.Partiallinearmodelstakethepragmaticpointoffixingtheerror
distributionbutlettheindexbeofnon-orsemiparametricstructure.General-
izedadditivemodelsconcentrateona(lowerdimensional)additivestructure
oftheindexwithfixedlinkfunction.Thismodelclassbalancesthedifficulty
ofhigh-dimensionalsmoothingwiththeflexibilityofnonparametrics.
In addition to the methodological aspects, the second part also covers
computationalalgorithmsfortheconsideredmodels.Asinthefirstpartwe
focus on cross-sectional data. It is intended to be used by Master and PhD
studentsorresearchers.
This book would not have been possible without substantial support
frommanycolleaguesandstudents.Ithasbenefitedatseveralstagesfrom
Preface VII
useful remarks and suggestions of our students at Humboldt-Universita¨t
zu Berlin, ENSAE Paris and Charles University Prague. We are grateful to
Lorens Helmchen, Stephanie Freese, Danilo Mercurio, Thomas Ku¨hn, Ying
Chen and Michal Benko for their support in text processing and program-
ming, Caroline Condron for language checking and Pavel Cˇ´ızˇek, Zdeneˇk
Hla´vkaandRainerSchulzfortheirassistanceinteaching.Weareindebtedto
JoelHorowitz(NorthwesternUniversity),EnnoMammen(Universita¨tHei-
delberg) and Helmut Rieder (Universita¨t Bayreuth) for their valuable com-
ments on earlier versions of the manuscript. Thanks go also to Clemens
Heine,SpringerVerlag,forbeingaverysupportiveandhelpfuleditor.
Berlin/Kaiserslautern/Madrid,February2004
WolfgangHa¨rdle
MarleneMu¨ller
StefanSperlich
AxelWerwatz
Contents
Preface ........................................................... V
Notation.......................................................... XXI
1 Introduction .................................................. 1
1.1 DensityEstimation........................................ 1
1.2 Regression ............................................... 3
1.2.1 ParametricRegression .............................. 5
1.2.2 NonparametricRegression .......................... 7
1.2.3 SemiparametricRegression ......................... 9
Summary ..................................................... 18
PartI NonparametricModels
2 Histogram .................................................... 21
2.1 MotivationandDerivation ................................ 21
2.1.1 Construction....................................... 21
2.1.2 Derivation ......................................... 23
2.1.3 VaryingtheBinwidth............................... 23
2.2 StatisticalProperties ...................................... 24
2.2.1 Bias ............................................... 25
2.2.2 Variance ........................................... 26
2.2.3 MeanSquaredError ................................ 27
2.2.4 MeanIntegratedSquaredError...................... 29
X Contents
2.2.5 OptimalBinwidth .................................. 29
2.3 DependenceoftheHistogramontheOrigin ................ 30
2.4 AveragedShiftedHistogram............................... 32
BibliographicNotes............................................ 35
Exercises...................................................... 36
Summary ..................................................... 38
3 NonparametricDensityEstimation............................. 39
3.1 MotivationandDerivation ................................ 39
3.1.1 Introduction ....................................... 39
3.1.2 Derivation ......................................... 40
3.1.3 VaryingtheBandwidth ............................. 43
3.1.4 VaryingtheKernelFunction ........................ 43
3.1.5 KernelDensityEstimationasaSumofBumps ........ 45
3.2 StatisticalProperties ...................................... 46
3.2.1 Bias ............................................... 46
3.2.2 Variance ........................................... 48
3.2.3 MeanSquaredError ................................ 49
3.2.4 MeanIntegratedSquaredError...................... 50
3.3 SmoothingParameterSelection ............................ 51
3.3.1 Silverman’sRuleofThumb ......................... 51
3.3.2 Cross-Validation ................................... 53
3.3.3 RefinedPlug-inMethods............................ 55
3.3.4 AnOptimalBandwidthSelector?! ................... 56
3.4 ChoosingtheKernel ...................................... 57
3.4.1 CanonicalKernelsandBandwidths.................. 57
3.4.2 AdjustingBandwidthsacrossKernels................ 59
3.4.3 OptimizingtheKernel .............................. 60
3.5 ConfidenceIntervalsandConfidenceBands ................ 61
3.6 MultivariateKernelDensityEstimation..................... 66
3.6.1 Bias,VarianceandAsymptotics...................... 70
3.6.2 BandwidthSelection................................ 72
3.6.3 ComputationandGraphicalRepresentation .......... 75
BibliographicNotes............................................ 79
Exercises...................................................... 80
Summary ..................................................... 82
Contents XI
4 NonparametricRegression .................................... 85
4.1 UnivariateKernelRegression .............................. 85
4.1.1 Introduction ....................................... 85
4.1.2 KernelRegression .................................. 88
4.1.3 Local Polynomial Regression and Derivative
Estimation ....................................... 94
4.2 OtherSmoothers.......................................... 98
4.2.1 Nearest-NeighborEstimator ........................ 98
4.2.2 MedianSmoothing ................................. 101
4.2.3 SplineSmoothing .................................. 101
4.2.4 OrthogonalSeries .................................. 104
4.3 SmoothingParameterSelection ............................ 107
4.3.1 ACloserLookattheAveragedSquaredError ........ 110
4.3.2 Cross-Validation ................................... 113
4.3.3 PenalizingFunctions ............................... 114
4.4 ConfidenceRegionsandTests.............................. 118
4.4.1 PointwiseConfidenceIntervals...................... 119
4.4.2 ConfidenceBands .................................. 120
4.4.3 HypothesisTesting ................................. 124
4.5 MultivariateKernelRegression ............................ 128
4.5.1 StatisticalProperties ................................ 130
4.5.2 PracticalAspects ................................... 132
BibliographicNotes............................................ 135
Exercises...................................................... 137
Summary ..................................................... 139
PartII SemiparametricModels
5 SemiparametricandGeneralizedRegressionModels ........... 145
5.1 DimensionReduction ..................................... 145
5.1.1 VariableSelectioninNonparametricRegression ...... 148
5.1.2 NonparametricLinkFunction ....................... 148
5.1.3 Semi-orNonparametricIndex ...................... 149
5.2 GeneralizedLinearModels ................................ 151
5.2.1 ExponentialFamilies ............................... 151
5.2.2 LinkFunctions ..................................... 153