Biased Sampling, Over-identified Parameter Problems and Beyond

Table of Contents

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   Book Overview
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   Chapter 1 Examples and Basic Theories for Length Biased Sampling Problems
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   Chapter 2 Brief Introduction of Renewal Process
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   Chapter 3 Heuristical Introduction of General Biased Sampling with Various Applications
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   Chapter 4 Brief Review of Parametric Likelihood Inferences
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   Chapter 5 Optimal Estimating Function Theory
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   Chapter 6 Projection Methods in General Semiparametric Models
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   Chapter 7 Generalized Method of Moments
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   Chapter 8 Empirical Likelihood with Applications
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    Chapter 9 Kullback–Leibler Likelihood and Entropy Family
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    Chapter 10 General Theory on Biased Sampling Problems
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    Chapter 11 General Theory for Case-Control Studies
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    Chapter 12 Conditioning Approach for Discrete Outcome Problems
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    Chapter 13 Discrete Data Models
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    Chapter 14 Gene and Environment Independence and Secondary Outcome Analysis in Case-Control Study
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    Chapter 15 Outcome Dependent Sampling and Maximum Rank Estimation
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    Chapter 16 Noncentral Hypergeometric Distribution and Poisson Binomial Distribution
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    Chapter 17 Inferences and Tests in Semiparametric Finite Mixture Models
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    Chapter 18 Connections Among Marginal Likelihood, Conditional Likelihood and Empirical Likelihood
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    Chapter 19 Causal Inference and Missing Data Problems
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    Chapter 20 Inference in Finite Populations
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    Chapter 21 Inference for Density Ratio Model with Continuous Covariates
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    Chapter 22 Non-ignorable Missing Data Problems
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    Chapter 23 Maximum Likelihood Estimation in Capture-Recapture Models
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    Chapter 24 A Review of Survival Analysis
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    Chapter 25 Length Biased Sampling, Multiplicative Censoring and Survival Analysis
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    Chapter 26 Applications of the Pool Adjacent Violation Algorithm (PAVA) in Statistical Inferences