<P><FONT size=3>BOOK：Applied Smoothing Techniques for Data Analysis- -The Kernel Approach with S-Plus Illustrations </FONT></P>
<P><FONT color=#0000ff>DJVU格式</FONT></P>
<P>CONTENTS <br>Density estimation for exploring data 1 <br>1.1 Introduction 1 <br>1.2 Basic ideas 1 <br>1.3 Density estimation in two dimensions 6 <br>1.4 Density estimation in three dimensions 10 <br>1.5 Directional data 12 <br>1.6 Data with bounded support 14 <br>1.7 Alternative forms of density estimation 17 <br>1.7.1 Variable bandwidths 17 <br>1.7.2 Nearest neighbour methods 18 <br>1.7.3 Orthogonal series methods 19 <br>1.7.4 Local likelihood and semiparametric density estima- <br>estimation 20 <br>1.8 Further reading 21 <br>Exercises 22 <br>Density estimation for inference 25 <br>2.1 Introduction 25 <br>2.2 Basic properties of density estimates 25 <br>2.3 Confidence and variability bands 29 <br>2.4 Methods of choosing a smoothing parameter 31 <br>2.4.1 Optimal smoothing 31 <br>2.4.2 Normal optimal smoothing 31 <br>2.4.3 Cross-validation 32 <br>2.4.4 Plug-in bandwidths 34 <br>2.4.5 Discussion 34 <br>2.5 Testing normality 38 <br>2.6 Normal reference band 41 <br>2.7 Testing independence 42 <br>2.8 The bootstrap and density estimation 44 <br>2.9 Further reading 46 <br>Exercises 46 <br>Nonparametric regression for exploring data 48 &gt;&gt;&gt;&gt;&gt;&gt;&gt;</P><br>

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