© 2017

Biased Sampling, Over-identified Parameter Problems and Beyond


  • Provides a comprehensive overview of traditional statistical methods such as likelihood based inference and estimating function theory

  • Extensively discusses many different biased sampling problems

  • Explicitly addresses the connections between Godambe’s estimating function theory, Hansen’s generalized method of moments, and Qin and Lawless’ empirical likelihood approach for over-identified parameter problems

  • Makes the general theory of biased sampling accessible to upper undergraduate and graduate students


Part of the ICSA Book Series in Statistics book series (ICSABSS)

Table of contents

About this book


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

Authors and affiliations

  1. 1.Biostatistics Research BranchNational Institute of Allergy and Infect Biostatistics Research BranchBethesdaUSA

About the authors

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.

Bibliographic information

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“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)