内容简介
This book is composed of ten chapters. The first chapter contains the preliminary knowledge about empirical likelihood and other relevant nonparametric methods. Chapters 2 and 3 analyze the section-data using the single-index model and the partially linear single-index model. Chapters 4 through 6 investigate the longitudinal data using the partially linear model, the varying coefficient model and a nonparametric regression model. Chapter 7 discusses nonlinear errors-in-covariables models with validation data. Chapters 8 through 10 investigate missing data under the framework of the linear model, a nonparametric regression model and the partially linear model. Every chapter, except for Chapter 1, of this book is self-contained so that the reader could focus on any chapter without much effect on the understanding of the others, and hence can read any chapters according to reader's own interest. The emphasis of this book is on methodolog..
目录
Preface
Chapter 1 Preliminary knowledge
1.1 Empirical likelihood (EL)
1.2 Bootstrap method
1.3 Smoothing methods
1.4 Cross-validation
1.5 Data sets
1.6 Some notations
Chapter 2 EL for single-index models
2.1 Introduction
2.2 Methods and results
2.3 Simulation results
2.4 Proofs
Chapter 3 EL in a partially linear single-index model
3.1 Introduction
3.2 Methodology
3.3 Simulation results
3.4 Proofs
Chapter 4 EL semiparametric regression analysis
4.1 Introduction
4.2 Maximum EL estimator
4.3 Confidence regions for regression coefficients
4.4 Confidence intervals for baseline function
4.5 Numerical results
4.6 Proofs
Chapter 5 EL for a varying coefficient model
5.I Introduction
5.2 Naive EL and maximum EL estimation
5.3 Two bias corrections
5.4 Asymptotic confidence regions
5.5 Numerical results
5.6 Proofs of Theorems
Chapter 6 EL local polynomial regression analysis
6.1 In..
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