Multivariate Nonparametric Regression and Visualization － With R and Applications to Finance（英文清晰版）
contents

Preface xvii
Introduction xix
1.1 Estimation of Functionals of Conditional Distributions xx
1.2 Quantitative Finance xxi
1.3 Visualization xxi
1.4 Literature xxiii
PART I METHODS OF REGRESSION AND CLASSIFICATION
1 Overview of Regression and Classification 3  
2 Linear Methods and Extensions 77  
3 Kernel Methods and Extensions 127  
4 Semiparametric and Structural Models 229  
5 Empirical Risk Minimization 241  
6 Visualization of Data 277  
7 Visualization of Functions 295  
Appendix A: R Tutorial 329
A.1 Data Visualization 329
A. 1.1 QQ Plots 329
A. 1.2 Tail Plots 330
A. 1.3 Two-Dimensional Scatter Plots 330
A. 1.4 Three-Dimensional Scatter Plots 331
A.2 Linear Regression 331
A.3 Kernel Regression 332
A.3.1 One-Dimensional Kernel Regression 332
A.3.2 Moving Averages 333
A.3.3 Two-Dimensional Kernel Regression 334
A.3.4 Three- and Higher-Dimensional Kernel Regression 336
A.3.5 Kernel Estimator of Derivatives 338
A.3.6 Combined State- and Time-Space Smoothing 340
A.4 Local Linear Regression 341
A.5 Additive Models: Backfitting 344
A.6 Single-Index Regression 345
A.6.1 Estimating the Index 346
A.6.2 Estimating the Link Function 346
A.6.3 Plotting the Single-Index Regression Function 346
A.7 Forward Stagewise Modeling 347
A.7.1 Stagewise Fitting of Additive Models 347
A.7.2 Projection Pursuit Regression 348
A.8 Quantile Regression 349
A.8.1 Linear Quantile Regression 349
A.8.2 Kernel Quantile Regression 349
References 351