BOOK：Applied Smoothing Techniques for Data Analysis - -The Kernel Approach with S-Plus Illustrations

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CONTENTS
Density estimation for exploring data 1
1.1 Introduction 1
1.2 Basic ideas 1
1.3 Density estimation in two dimensions 6
1.4 Density estimation in three dimensions 10
1.5 Directional data 12
1.6 Data with bounded support 14
1.7 Alternative forms of density estimation 17
1.7.1 Variable bandwidths 17
1.7.2 Nearest neighbour methods 18
1.7.3 Orthogonal series methods 19
1.7.4 Local likelihood and semiparametric density estima-
estimation 20
1.8 Further reading 21
Exercises 22
Density estimation for inference 25
2.1 Introduction 25
2.2 Basic properties of density estimates 25
2.3 Confidence and variability bands 29
2.4 Methods of choosing a smoothing parameter 31
2.4.1 Optimal smoothing 31
2.4.2 Normal optimal smoothing 31
2.4.3 Cross-validation 32
2.4.4 Plug-in bandwidths 34
2.4.5 Discussion 34
2.5 Testing normality 38
2.6 Normal reference band 41
2.7 Testing independence 42
2.8 The bootstrap and density estimation 44
2.9 Further reading 46
Exercises 46
Nonparametric regression for exploring data 48 >>>>>>>

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