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Table of Contents
1 Cover
5 Preface
7 Notation
8 1 Introduction 1.1 Random Truncation 1.2 One‐sided Truncation 1.3 Double Truncation 1.4 Real Data Examples References
9 2 One‐Sample Problems 2.1 Nonparametric Estimation of a Distribution Function 2.2 Semiparametric and Parametric Approaches 2.3 R Code for the Examples References
10 3 Smoothing Methods 3.1 Some Background in Kernel Estimation 3.2 Estimating the Density Function 3.3 Asymptotic Properties 3.4 Data‐driven Bandwidth Selection 3.5 Further Issues in Kernel Density Estimation 3.6 Estimating the Hazard Function 3.7 R Code for the Examples References
11 4 Regression Analysis 4.1 Observational Bias in Regression 4.2 Proportional Hazards Regression 4.3 Accelerated Failure Time Regression 4.4 Nonparametric Regression 4.5 R Code for the Examples References
12 5 Further Topics 5.1 Two‐Sample Problems 5.2 Competing Risks 5.3 Testing for Quasi‐independence 5.4 Dependent Truncation 5.5 R Code for the Examples References
13
A: Packages and Functions in R
A.1 Computing the NPMLE and Standard Errors
A.2 Assessing the Existence and Uniqueness of the NPMLE
A.3 Semiparametric and Parametric Estimation
A.4 Kernel Estimation
A.5 Regression Analysis
A.6 Competing Risks
A.7 Simulating Data
A.8 Testing Quasi‐independence
A.9 Dependent Truncation
References
14 Index
List of Tables
1 Chapter 1Table 1.1 Descriptive statistics for Childhood Cancer Data: sample size
S R...Table 1.4 Descriptive statistics for the Quasar Data. Luminosity in log‐scal...Table 1.5 Parkinson's Disease Data: age of onset for genetic groups. Early o...Table 1.6 Descriptive statistics for the ACS Data. Sample size
2 Chapter 2Table 2.1 (Example 2.1.19). Bias and standard deviation (SD) of
