Biased sampling, over-identified parameter problems and beyond /

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Bibliographic Details
Author / Creator:Qin, Jing, author.
Imprint:Cham : Springer, 2017.
Description:1 online resource
Language:English
Series:ICSA book series in statistics
ICSA book series in statistics.
Subject:Sampling (Statistics)
Applied mathematics.
Economic theory & philosophy.
Probability & statistics.
MATHEMATICS -- Probability & Statistics -- General.
Sampling (Statistics)
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11307989
Hidden Bibliographic Details
ISBN:9789811048562
9811048568
9789811048548
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (EBSCO, viewed June 21, 2017).
Summary: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.--
Other form:Print version: Qin, Jing. Biased sampling, over-identified parameter problems and beyond. Singapore : Springer, ©2017 xvi, 624 pages ICSA book series in statistics. 2199-0999 9789811048548
Table of Contents:
  • Preface; Acknowledgements; Contents; 1 Examples and Basic Theories for Length Biased Sampling Problems; 1.1 Length Biased Sampling Examples; 1.2 Basic Properties of Length Biased Sampling Problems; 1.3 Stochastic Ordering; 1.4 Lorenz Curve; 1.5 Characterization of Length Biased Distribution; 2 Brief Introduction of Renewal Process; 2.1 Basic Concepts; 2.2 Forward and Backward Recurrence Times; 2.3 Basic Results on Poisson Process; 3 Heuristical Introduction of General Biased Sampling with Various Applications ; 3.1 Natural Selection Biased Sampling Problems.
  • 3.2 Modelling Based Selection Biased Sampling Problems4 Brief Review of Parametric Likelihood Inferences; 4.1 Kullback
  • Leibler Information and Entropy Concepts; 4.2 Issues in Maximum Likelihood Estimation; 4.3 Popular Inference Methods in the Presence of Nuisance Parameters; 4.4 Quasi-likelihood Methods in Linear Regression Models; 4.5 Composite Likelihoods and Corrected Likelihoods; 4.6 Variable Selection and Akaike Criterion; 4.7 Two Useful Maximization Algorithms; 4.8 Likelihood Based Inference with Inequality Constraints; 5 Optimal Estimating Function Theory.
  • 5.1 Godambe's Optimality Criterion5.2 Applications of Godambe's Theory in Missing Covariate Problems; 5.3 Godambe's Theory in Length Biased Sampling AFT Models; 5.4 Ancillarity and Fisher Information with Nuisance Parameters; 5.5 Projection Methods in Parametric Models; 5.6 Reduce Sensitivity with Respect to Nuisance Parameters; 6 Projection Methods in General Semiparametric Models; 6.1 Projection Method for the Mean Estimation and Linear Regression Model; 6.2 Information Contained in the Conditional Expectation Model; 6.3 Projection Method in a Two Sample Density Ratio Model.
  • 6.4 Information Calculation in Over-identified Semiparametric Models6.5 Information Calculation for Missing Data Problems; 6.6 A Non-root n Consistent Estimator Example; 7 Generalized Method of Moments; 7.1 Basic Concepts on Generalized Method of Moments; 7.2 An Optimal Result Based on an Embed Exponential Family; 7.3 Applications of GMM; 8 Empirical Likelihood with Applications; 8.1 Definition of Empirical Likelihood and Basic Properties; 8.2 General Theory of Empirical Likelihood in Estimating Equations; 8.3 Miscellaneous Topics on Empirical Likelihood.
  • 8.4 Hybrid Likelihoods and Utilization Auxiliary Information8.5 Combine Summarized Information: A More Flexible Method in Meta Analysis; 9 Kullback
  • Leibler Likelihood and Entropy Family ; 9.1 Minimize Kullback
  • Leibler Divergence Subject to Moment Constraints; 9.2 Entropy Family in the Presence of Covariates; 9.3 Some Miscellaneous Results; 9.4 Entropy Family with Fixed Margins in Discrete Case; 9.5 Generalized Empirical Likelihoods; 9.6 Inference for Exponential Family with Specified Mean Function; 10 General Theory on Biased Sampling Problems.