Multivariate Nonparametric Regression and Visualization by Klemel, Jussi -- Read -- Imperial Library of Trantor

Index

Cover Half Title page Title page Copyright page Dedication Preface Introduction

I.1 Estimation of Functionals of Conditional Distributions I.2 Quantitative Finance I.3 Visualization I.4 Literature

Part I: Methods of Regression and Classification

Chapter 1: Overview of Regression and Classification

1.1 Regression 1.2 Discrete Response Variable 1.3 Parametric Family Regression 1.4 Classification 1.5 Applications in Quantitative Finance 1.6 Data Examples 1.7 Data Transformations 1.8 Central Limit Theorems 1.9 Measuring the Performance of Estimators 1.10 Confidence Sets 1.11 Testing

Chapter 2: Linear Methods and Extensions

2.1 Linear Regression 2.2 Varying Coefficient Linear Regression 2.3 Generalized Linear and Related Models 2.4 Series Estimators 2.5 Conditional Variance and ARCH Models 2.6 Applications in Volatility and Quantile Estimation 2.7 Linear Classifiers

Chapter 3: Kernel Methods and Extensions

3.1 Regressogram 3.2 Kernel Estimator 3.3 Nearest-Neighbor Estimator 3.4 Classification with Local Averaging 3.5 Median Smoothing 3.6 Conditional Density Estimation 3.7 Conditional Distribution Function Estimation 3.8 Conditional Quantile Estimation 3.9 Conditional Variance Estimation 3.10 Conditional Covariance Estimation 3.11 Applications in Risk Management 3.12 Applications in Portfolio Selection

Chapter 4: Semiparametric and Structural Models

4.1 Single-Index Model 4.2 Additive Model 4.3 Other Semiparametric Models

Chapter 5: Empirical Risk Minimization

5.1 Empirical Risk 5.3 Support Vector Machines 5.4 Stagewise Methods 5.5 Adaptive Regressograms

Part II: Visualization

Chapter 6: Visualization of Data

6.1 Scatter Plots 6.2 Histogram and Kernel Density Estimator 6.3 Dimension Reduction 6.4 Observations as Objects

Chapter 7: Visualization of Functions

7.1 Slices 7.2 Partial Dependence Functions 7.3 Reconstruction of Sets 7.4 Level Set Trees 7.5 Unimodal Densities

Appendix A: R Tutorial

A.1 Data Visualization A.2 Linear Regression A.3 Kernel Regression A.4 Local Linear Regression A.5 Additive Models: Backfitting A.6 Single-Index Regression A.7 Forward Stagewise Modeling A.8 Quantile Regression

References Author Index Topic Index