Biased Sampling, Over-identified Parameter Problems and Beyond

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.

Chapter 1. Some Examples on Biased Sampling Problems.- Chapter 2. Some Results in Parametric Likelihood and Estimating Functions.- Chapter 3.  Nonparametric Maximum Likelihood Estimation and Empirical Likelihood Method.- Chapter 4. General Results in Multiple Samples Biased Sampling Problems with Applications in Case and Control and Genetic Epidemiology.- Chapter 5. Outcome Dependent Sampling Problems.- Chapter 6. Missing Data Problem and Causal Inference.- Chapter 7.  Applications of Exponential Tilting Models in Finite Mixture Models.- Chapter 8.  Applications of Empirical Likelihood Methods in Survey Sampling.- Chapter 9. Some Other Topics.

“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)