Kernel Smoothing

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Author: M. P. Wand, M. C. Jones

• Publisher: Chapman and Hall/CRC; Softcover reprint of the original 1st ed. 1995 edition (Dec 1 1994)
• Language: English
• ISBN-10: 0412552701
• ISBN-13: 978-0412552700
• Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function.
  
  This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail.
  

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关键词：smoothing smooth kernel Thing Thin principles performed structure original provides

已有，但不是PDF：https://bbs.pinggu.org/thread-1105465-1-1.html
楼主的材料不错，高清的PDF。

Preface xi
1 Introduction 1
1.1 Introduction 1
1.2 Density estimation and histograms 5
1.3 About this book 7
1.4 Options for reading this book 9
1.5 Bibliographical notes 9
2 Univariate kernel density estimation 10
2.1 Introduction 10
2.2 The univariate kernel density estimator 11
2.3 The MSE and MISE criteria 14
2.4 Order and asymptotic notation; Taylor expansion 17
2.4.1 Order and asymptotic notation 17
2.4.2 Taylor expansion 19
2.5 Asymptotic MSE and MISE approximations 19
2.6 Exact MISE calculations 24
2.7 Canonical kernels and optimal kernel theory 28
2.8 Higher-order kernels 32
2.9 Measuring how difficult a density is to estimate 36
2.10 Modifications of the kernel density estimator 40
2.10.1 Local kernel density estimators 40
2.10.2 Variable kernel density estimators 42
2.10.3 Transformation kernel density estimators 43
2.11 Density estimation at boundaries 46
2.12 Density derivative estimation 49
2.13 Bibliographical notes 50
2.14 Exercises 52
3 Bandwidth selection 58
3.1 Introduction 58
3.2 Quick and simple bandwidth selectors 59
3.2.1 Nor mal scale rules 60
vii viii CONTENTS
3.2.2. Oversmoothed bandwidth selection rules 61
3.3 Least squares cross-validation 63
3.4 Biased cross-validation 65
3.5 Estimation of density functionals 67
3.6 Plug-in bandwidth selection 71
3.6.1 Direct plug-in rules 71
3.6.2 Solve-the-equation rules 74
3. 7 Smoothed cross-validation bandwidth selection 75
3.8 Comparison of bandwidth selectors 79
3.8.1 Theoretical performance 79
3.8.2 Practical advice 85
3.9 Bibliographical notes 86
3.10 Exercises 88
4 Multivariate kernel density estimation 90
4.1 Introduction 90
4.2 The multivariate kernel density estimator 91
4.3 Asymptotic MISE approximations 94
4.4 Exact MISE calculations 101
4.5 Choice of a multivariate kernel 103
4.6 Choice of smoothing parametrisation 105
4. 7 Bandwidth selection 108
4.8 Bibliographical notes 110
4.9 Exercises 110
5 Kernel regression 114
5.1 Introduction 114
5.2 Local polynomial kernel estimators 116
5.3 Asymptotic MSE approximations: linear case 120
5.3.1 Fixed equally spaced design 120
5.3.2 Random design 123
5.4 Asymptotic MSE approximations: general case 125
5.5 Behaviour near the boundary 126
5.6 Comparison with other kernel estimators 130
5.6.1 Asymptotic comparison 130
5.6.2 Effective kernels 133
5. 7 Derivative estimation 135
5.8 Bandwidth selection 138
5.9 Multivariate nonparametric regression 140
5.10 Bibliographical notes 141
5.11 Exercises 143
6 Selected extra topics 146
6.1 Introduction 146
6.2 Kernel density estimation in other settings 147 CONTENTS
6.2.1 Dependent data
6.2.2 Length biased data
6.2.3 Right-censored data
6.2.4 Data measured with error
6.3 Hazard function estimation
6.4 Spectral density estimation
6.5 Likelihood-based regression models
6.6 Intensity function estimation
6. 7 Bibliographical notes
6.8 Exercises
ix
147
150
154
156
160
162
164
167
169
170
Appendices 172
A Notation 172
B Tables 175
C Facts about normal densities 177
C.1 Univariate normal densities 177
C.2 Multivariate normal densities 180
C.3 Bibliographical notes 181
D Computation of kernel estimators 182
D.1 Introduction 182
D.2 The binned kernel density estimator 183
D.3 Computation of kernel functional estimates 188
D.4 Computation of kernel regression estimates 189
D.5 Extension to multivariate kernel smoothing 191
D.6 Computing practicalities 192
D.7 Bibliographical notes 192
References 193
Index 208

我买完了 怎么无法下载， 我第一次注册给了10个比， 现在只有一个了 。 怎么办

顶楼主，好赞~\(≧▽≦)/~

这本书一直想看，就是有点太贵啦

感谢楼主，很好！

完美，非拍照版，已买（PS:楼主哪弄的啊。。。)

感谢楼主，资料完美，赞！

怎么下载

感谢分享，在了解Computer Vision的时候碰到了Kernal Smoothing的概念，借这个机会长长见识