Biased Sampling, Over-identified Parameter Problems and Beyond

Author:   Jing Qin
Publisher:   Springer Verlag, Singapore
Edition:   Softcover reprint of the original 1st ed. 2017
ISBN:  

9789811352492


Pages:   624
Publication Date:   09 December 2018
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Biased Sampling, Over-identified Parameter Problems and Beyond


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Overview

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. 

Full Product Details

Author:   Jing Qin
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   Softcover reprint of the original 1st ed. 2017
Weight:   0.967kg
ISBN:  

9789811352492


ISBN 10:   9811352496
Pages:   624
Publication Date:   09 December 2018
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

Reviews

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)


Author Information

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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