Nonparametric Models for Longitudinal Data : With Implementation in R

Cover
 Half title
 MONOGRAPHS ON STATISTICS ANDAPPLIED PROBABILITY
 Title
 copyright
 Dedication
 Contents
 List of Figures
 List of Tables
 Preface
 About the Authors
 I Introduction and Review
 1 Introduction
 1.1 Scientific Objectives of Longitudinal Studies
 1.2 Data Structures and Examples
 1.2.1 Structures of Longitudinal Data
 1.2.2 Examples of Longitudinal Studies
 1.2.3 Objectives of Longitudinal Analysis
 1.3 Conditional-Mean Based Regression Models
 1.3.1 Parametric Models
 1.3.2 Semiparametric Models
 1.3.3 Unstructured Nonparametric Models
 1.3.4 Structured Nonparametric Models. 1.4 Conditional-Distribution Based Models1.4.1 Conditional Distribution Functions and Functionals
 1.4.2 Parametric Distribution Models
 1.4.3 Semiparametric Distribution Models
 1.4.4 Unstructured Nonparametric Distribution Models
 1.4.5 Structured Nonparametric Distribution Models
 1.5 Review of Smoothing Methods
 1.5.1 Local Smoothing Methods
 1.5.2 Global Smoothing Methods
 1.6 Introduction to R
 1.7 Organization of the Book
 2 Parametric and Semiparametric Methods
 2.1 Linear Marginal and Mixed-Effects Models
 2.1.1 Marginal Linear Models
 2.1.2 The Linear Mixed-Effects Models. 2.1.3 Conditional Maximum Likelihood Estimation2.1.4 Maximum Likelihood Estimation
 2.1.5 Restricted Maximum Likelihood Estimation
 2.1.6 Likelihood-Based Inferences
 2.2 Nonlinear Marginal and Mixed-Effects Models
 2.2.1 Model Formulation and Interpretation
 2.2.2 Likelihood-Based Estimation and Inferences
 2.2.3 Estimation of Subject-Specific Parameters
 2.3 Semiparametric Partially Linear Models
 2.3.1 Marginal Partially Linear Models
 2.3.2 Mixed-Effects Partially Linear Models
 2.3.3 Iterative Estimation Procedure
 2.3.4 Profile Kernel Estimators. 2.3.5 Semiparametric Estimation by Splines2.4 R Implementation
 2.4.1 The BMACS CD4 Data
 2.4.2 The ENRICHD BDI Data
 2.5 Remarks and Literature Notes
 II Unstructured Nonparametric Models
 3 Kernel and Local Polynomial Methods
 3.1 Least Squares Kernel Estimators
 3.2 Least Squares Local Polynomial Estimators
 3.3 Cross-Validation Bandwidths
 3.3.1 The Leave-One-Subject-Out Cross-Validation
 3.3.2 A Computation Procedure for Kernel Estimators
 3.3.3 Heuristic Justification of Cross-Validation
 3.4 Bootstrap Pointwise Confidence Intervals
 3.4.1 Resampling-Subject Bootstrap Samples. 3.4.2 Two Bootstrap Confidence Intervals3.4.3 Simultaneous Confidence Bands
 3.5 R Implementation
 3.5.1 The HSCT Data
 3.5.2 The BMACS CD4 Data
 3.6 Asymptotic Properties of Kernel Estimators
 3.6.1 Mean Squared Errors
 3.6.2 Assumptions for Asymptotic Derivations
 3.6.3 Asymptotic Risk Representations
 3.6.4 Useful Special Cases
 3.7 Remarks and Literature Notes
 4 Basis Approximation Smoothing Methods
 4.1 Estimation Method
 4.1.1 Basis Approximations and Least Squares
 4.1.2 Selecting Smoothing Parameters
 4.2 Bootstrap Inference Procedures
 4.2.1 Pointwise Confidence Intervals.