Smoothing Techniques: With Implementation in S

266 Pages·1991·4.955 MB·English

by  Wolfgang Härdle (auth.)

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Wolfgang HardIe
Smoothing Techniques
S
With Implementation in
With 87 Illustrations
Springer-Verlag
New York Berlin Heidelberg London
Paris Tokyo Hong Kong Barcelona
Wolfgang Hardie
Center for Operations Research
and Econometrics
Universite Catholique de Louvain
B-1348 Louvain-La-Neuve
Belgium
Mathematics Subject Classification (1980): 62-07, 62G05, 62G25
Library of Congress Cataloging-in-Publication Data
Hardie, Wolfgang.
Smoothing techniques : with implementation in S I
Wolfgang Hardie.
p. cm.
Includes index.
l. Smoothing (Statistics) 2. Mathematical statistics-Data
processing. I. Title.
QA278.H348 1990 90-47545
519.5--dc20 CIP
Printed on acid-free paper.
© 1991 Springer-Verlag New York Inc.
Softcover reprint of the hardcover 1s t edition 1991
All rights reserved. This work may not be translated or copied in whole or in part without the written
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Photocomposed using the author's TEX files.
Printed and bound by R. R. Donnelley and Sons, Harrisonburg, Virginia.
987654321
ISBN-13: 978-1-4612-8768-1 e-ISBN-13: 978-1-4612-4432-5
DOl: 10.1007/978-1-4612-4432-5
For Renate, Nora, Viola, and Adrian
Preface
The aim of this book is two-pronged. It addresses two different
groups of readers. The one is more theoretically oriented, interested in
the mathematics of smoothing techniques. The other comprises statisti
cians leaning more towards applied aspects of a new methodology. Since
with this book I want to address both sides, this duality is also in the
title: Smoothing techniques are presented both in theory and in a mod
em computing environment on a workstation, namely the new S. I hope
to satisfy both groups of interests by an almost parallel development of
theory and practice in S.
It is my belief that this monograph can be read at different levels. In
particular it can be used in undergraduate teaching since most of it is at
the introductory level. I wanted to offer a text that provides a nontech
nical introduction to the area of nonparametric density and regression
function estimation. However, a statistician more trained in parametric
statistics will find many bridges between the two apparently different
worlds presented here. Students interested in application of the meth
ods may employ the included S code for the estimators and techniques
shown. In particular, I have put emphasis on the computational aspects
of the algorithms.
Smoothing in high dimensions confronts the problem of data sparse
ness. A principal feature of smoothing, the averaging of data points in a
prescribed neighborhood, is not really practicable in dimensions greater
than three if we just have 100 data points. Additive models guide a
way out of this dilemma but require for their interactiveness and re
cursiveness highly effective algorithms. For this purpose, the method
of WARPing is described in great detail. WARPing does not mean that
the data are warped. Rather, it is an abbreviation of Weighted Averag
ing using Rounded Points. This technique is based on discretizing the
data first into a finite grid of bins and then smoothing the binned data.
The computational effectiveness lies in the fact that now the smooth
ing (weighted averaging) is performed on a much smaller number of
(rounded) data points.
This text has evolved out of several classes that I taught at different
universities-Bonn, Dortmund, and Santiago de Compostella. I would
like to thank the students at these institutions for their cooperation and
criticisms that formed and shaped the essence of the book. In partic
ular, I would like to thank Klaus Breuer and Andreas Krause from the
vii
viii Preface
University of Dortmund for their careful typing and preparation of the
figures and the Sand C programs.
This book would not have been possible without the generous sup
port of FriedheIm Eicker. The technique of WARPing was developed
jointly with David Scott; his view on computer-assisted smoothing
methods helped me in combining theory and S. Discussions with
Steve Marron shaped my sometimes opaque opinion about selection
of smoothing parameters. I would like to thank these people for their
collaboration and the insight they provided into the field of smoothing
techniques.
Finally I gratefully acknowledge the financial support of the Deut
sche Forschungsgemeinschaft (Sonderforschungsbereich 303) and of the
Center for Operations Research and Econometrics (CORE).
Louvain-La-Neuve, January 1990 Wolfgang HardIe
Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vll
I. Density Smoothing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1. The Histogram............................................ 3
1.0 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 Definitions of the Histogram. . . . . . . . . . . . . . . . . . . . . . . 4
The Histogram as a Frequency Counting Curve. . . . 6
The Histogram as a Maximum Likelihood Estimate 9
Varying the Binwidth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.2 Statistics of the Histogram. . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3 The Histogram in S ............................... 18
1.4 Smoothing the Histogram by WARPing............ 27
WARPing Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
WARPing in S .................................... 34
Exercises. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2. Kernel Density Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.0 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.1 Definition of the Kernel Estimate. . . . . . . . . . . . . . . . . . 44
Varying the Kernel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Varying the Bandwidth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.2 Kernel Density Estimation in S ................... 49
Direct Algorithm................................... 49
Implementation in S .............................. 50
2.3 Statistics of the Kernel Density. . . . . .. . . . . . . . . . . . . .. 54
Speed of Convergence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Confidence Intervals and Confidence Bands. . . . . . . . 62
2.4 Approximating Kernel Estimates by WARPing.. . .. 67
2.5 Comparison of Computational Costs. . . . . . . . . . . . . . . 69
2.6 Comparison of Smoothers Between Laboratories. . . 72
Keeping the Kernel Bias the Same. . . . . . . . . . . . . . . . . 72
Keeping the Support of the Kernel the Same. . . . . . . 72
Canonical Kernels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
2.7 Optimizing the Kernel Density. . . .. . .. . . .. . . . . . . . .. 76
2.8 Kernels of Higher Order ............ '" ............. 78
2.9 Multivariate Kernel Density Estimation. . . . .. . . . . .. 79
Same Bandwidth in Each Component. . . . . . . . . . . . . . 79
ix
x Contents
Nonequal Bandwidths in Each Component. ....... . 81
A Matrix of Bandwidths ........................... . 83
Exercises .......................................... . 83
3. Further Density Estimators ............................... . 85
3.0 Introduction ....................................... . 85
3.1 Orthogonal Series Estimators ...................... . 85
3.2 Maximum Penalized Likelihood Estimators ....... . 88
Exercises .......................................... . 88
4. Bandwidth Selection in Practice ........................... . 90
4.0 Introduction ....................................... . 90
4.1 Kernel Estimation Using Reference Distributions .. 90
4.2 Plug-In Methods .................................. . 92
4.3 Cross-Validation ................................... . 92
4.3.1 Maximum Likelihood Cross-Validation ........... . 93
Direct Algorithm .................................. . 95
4.3.2 Least-Squares Cross-Validation .................... . 95
Direct Algorithm .................................. . 99
4.3.3 Biased Cross-Validation ........................... . 100
Algorithm ......................................... . 101
4.4 Cross-Validation for WARPing Density Estimation. 103
4.4.1 Maximum Likelihood Cross-Validation ........... . 103
4.4.2 Least-Squares Cross-Validation .................... . 103
Algorithm ......................................... . 104
Implementation in S ............................. . 106
4.4.3 Biased Cross-Validation ........................... . 111
Algorithm ......................................... . 113
Implementation in S ............................. . 114
Exercises .......................................... . 118
II. Regression Smoothing ..................................... . 121
5. Nonparametric Regression ................................ . 123
5.0 Introduction ....................................... . 123
5.1 Kernel Regression Smoothing ..................... . 126
5.1.1 The Nadaraya-Watson Estimator .................. . 126
Direct Algorithm .................................. . 127
Implementation in S ............................. . 128
5.1.2 Statistics of the Nadaraya-Watson Estimator ...... . 132
5.1.3 Confidence Intervals .............................. . 135
5.1.4 Fixed Design Model. .............................. . 136
Contents xi
5.1.5 The WARPing Approximation .................... . 137
Basic Algorithm ................................... . 138
Implementation in S ............................. . 139
5.2 k-Nearest Neighbor (k-NN) ........................ . 143
5.2.1 Definition of the k-NN Estimate .................. . 143
5.2.2 Statistics of the k-NN Estimate .................... . 146
5.3 Spline Smoothing ................................. . 147
Exercises .......................................... . 149
6. Bandwidth Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.0 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.1 Estimates of the Averaged Squared Error.. . . . . . . . . . 152
6.1.0 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
6.1.1 Penalizing Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
6.1.2 Cross-Validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
Direct Algorithm................................... 159
6.2 Bandwidth Selection with WARPing............... 160
Penalizing Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Cross-Validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Basic Algorithm ................................... ,. 162
Implementation in S .............................. 163
Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1
7. Simultaneous Error Bars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
7.1 Golden Section Bootstrap.......................... 173
Algorithm for Golden Section Bootstrapping. . .. . .. 174
Implementation in S .............................. 177
7.2 Construction of Confidence Intervals. . . . . . . . . . . . . . . 186
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Tables. ............ ............................ ...... ...... 199
Solutions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
List of Used S Commands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Symbols and Notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
Subject Index. .. .. . ... .. . .. . .. .. . . .. .. . .. .. ... . .. ... ... . ... 257