Qin Biased Sampling, Over-identified Parameter Problems and Beyond

1. Auflage 2017
ISBN: 978-981-10-4856-2
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 624 Seiten

Reihe: ICSA Book Series in Statistics

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. 

Zielgruppe

Research

Autoren/Hrsg.

Qin, Jing

Inhaltsverzeichnis

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.

Über Autor(innen)

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.