Biased Sampling, Over-identified Parameter Problems and Beyond

1. Front Matter

   Pages i-xvi

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2. Examples and Basic Theories for Length Biased Sampling Problems

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   Pages 1-9

3. Brief Introduction of Renewal Process

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   Pages 11-21

4. Heuristical Introduction of General Biased Sampling with Various Applications

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   Pages 23-47

5. Brief Review of Parametric Likelihood Inferences

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   Pages 49-83

6. Optimal Estimating Function Theory

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   Pages 85-110

7. Projection Methods in General Semiparametric Models

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   Pages 111-127

8. Generalized Method of Moments

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   Pages 129-138

9. Empirical Likelihood with Applications

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   Pages 139-170

10. Kullback–Leibler Likelihood and Entropy Family

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    Pages 171-189

11. General Theory on Biased Sampling Problems

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    Pages 191-206

12. General Theory for Case-Control Studies

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    Pages 207-239

13. Conditioning Approach for Discrete Outcome Problems

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    Pages 241-247

14. Discrete Data Models

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    Pages 249-257

15. Gene and Environment Independence and Secondary Outcome Analysis in Case-Control Study

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    Pages 259-279

16. Outcome Dependent Sampling and Maximum Rank Estimation

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    Pages 281-295

17. Noncentral Hypergeometric Distribution and Poisson Binomial Distribution

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    Pages 297-305

18. Inferences and Tests in Semiparametric Finite Mixture Models

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    Pages 307-330

19. Connections Among Marginal Likelihood, Conditional Likelihood and Empirical Likelihood

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    Pages 331-351

20. Causal Inference and Missing Data Problems

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    Pages 353-408

This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc.


The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others. 

• Biased Sampling Problems
• Finite Mixture Models
• Genetic Epidemiology
• Parametric Likelihood
• Survey Sampling

“This book is the first comprehensive overview of which I am aware that shows how statistical methods such as empirical likelihood and generalized method of moments can be appropriately and efficiently used in the over-identified parameter problem. … With its extensive exercises and easy style, this book is suitable as an upper-level textbook for graduate students or as a reference book for workshops that target postdoctoral fellows and junior researchers.” (JingNing, Journal of the American Statistical Association JASA, Vol. 113 (522), 2018)

• Biostatistics Research Branch, National Institute of Allergy and Infect Biostatistics Research Branch, Bethesda, USA

  Jing Qin

Dr. Jing Qin currently serves as a Mathematical Statistician at the National Institute of Allergy and Infectious Diseases (NIAID). He received his Ph.D. in Statistics from the University of Waterloo, Canada and completed his postdoctoral studies at Stanford University and the University of Waterloo. His research interests include case-control studies, epidemiology studies, missing data analysis, causal inference, and related applied problems.

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