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.
Recenzijas
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)
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.
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.
Biežāk uzdotie jautājumi par e-grāmatām