Parimal Mukhopadhyay

Topics in Survey Sampling (eBook, PDF)

• Format: PDF

Produktbeschreibung

The aim of this book is to make a comprehensive review of some of the research topics in the area of survey sampling. The book will be of interest to theoretical and applied statisticians.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 292
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461220886
• Artikelnr.: 43993006

Inhaltsangabe

1 The Basic Concepts.- 1.1 Introduction.- 1.2 The Fixed Population model.- 1.3 Different Types of Sampling Designs.- 1.4 The Estimators.- 1.5 Some Inferential Problems under Fixed Population Set-Up.- 1.6 Plan of the Book.- 2 Inference under Frequentist Theory Approach.- 2.1 Introduction.- 2.2 Principles of Inference Based on Theory of Prediction.- 2.3 Robustness of Model-Dependent Optimal Strategies.- 2.4 A Class of Predictors under Model ?(X, v).- 2.5 Asymptotic Unbiased Estimation of Design-Variance of $${{hat{T}}_{{GR}}}$$.- 3 Bayes and Empirical Bayes Prediction of a Finite Population Total.- 3.1 Introduction.- 3.2 Bayes and Minimax Prediction of Finite Population Parameters.- 3.3 Bayes Prediction of a Finite Population Total under Normal Regression Model.- 3.4 Bayes Prediction under an Asymmetric Loss Function.- 3.5 James-Stein Estimator and Associated Estimators.- 3.6 Empirical Bayes Prediction of Population Total under Simple Location Model.- 3.7 EB-Prediction under Normal Model using Covariates.- 3.8 Applications in Small Area Estimation.- 3.9 Bayes Prediction under Random Error Variance Model.- 3.10 Exercises.- 4 Modifications of Bayes Procedure.- 4.1 Introduction.- 4.2 Linear Bayes Prediction.- 4.3 Restricted Linear Bayes Prediction.- 4.4 Constrained Bayes Prediction.- 4.5 Bayesian Robustness under a Class of Alternative Models.- 4.6 Robust Bayes Estimation under Contaminated Priors.- 4.7 Exercises.- 5 Estimation of Finite Population Variance, Regression Coefficient.- 5.1 Introduction.- 5.2 Design-Based Estimation of a Finite Population Variance.- 5.3 Model-Based Prediction of V.- 5.4 Bayes Prediction of V(y).- 5.5 Asymptotic Properties of Sample Regression Coefficient.- 5.6 PM-Unbiased Estimation of Slope Parameters in the Linear Regression Model.- 5.7Optimal Prediction of Finite Population Regression Coefficient under Multiple Regression Model.- 5.8 Exercises.- 6 Estimation of a Finite Population Distribution Function.- 6.1 Introduction.- 6.2 Design-Based Estimators.- 6.3 Model-Based Predictors.- 6.4 Conditional Approach.- 6.5 Asymptotic Properties of the Estimators.- 6.6 Non-Parametric Kernel Estimators.- 6.7 Desirable Properties of an Estimator.- 6.8 Empirical Studies.- 6.9 Best Unbiased Prediction (BUP) under Gaussian Superpopulation Model.- 6.10 Estimation of Median.- 7 Prediction in Finite Population under Measurement Error Models.- 7.1 Introduction.- 7.2 Additive Measurement Error Models.- 7.3 Prediction under Multiplicative Error-in-Variables Model.- 7.4 Exercises.- 8 Miscellaneous Topics.- 8.1 Introduction.- 8.2 Calibration Estimators.- 8.3 Post-Stratification.- 8.4 Design-Based Conditional Unbiasedness.- 8.5 Exercises.- References.- Author Index.