Parimal Mukhopadhyay

Topics in Survey Sampling

• Broschiertes Buch

Produktbeschreibung

The aim of this book is to make a comprehensive review on some of the research topics in the area of survey sampling which has not been covered in any book yet. The proposed book aims at making a comprehensive review of applications of Bayes procedures, Empirical Bayes procedures and their ramifications (like linear Bayes estimation, restricted Bayes least square prediction, constrained Bayes estimation, Bayesian robustness) in making inference from a finite population sampling. Parimal Mukhopadhyay is Professor at the Indian Statistical Institute (ISI), Calcutta. He received his Ph.D. degree in Statistics from the University of Calcutta in 1977. He also served as a faculty member in the University of Ife, Nigeria, Moi University, Kenya, University of South Pacific, Fiji Islands and held visiting positions at University of Montreal, University of Windsor, Stockholm University, University of Western Australia, etc. He has to his credit more than fifty research papers in Survey Sampling, some co-authored, three text books on Statistics and three research monographs in Survey Sampling. He is a member of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.

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Produktdetails

• Lecture Notes in Statistics .153
• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-0-387-95108-9
• Softcover reprint of the original 1st ed. 2000
• Seitenzahl: 308
• Erscheinungstermin: 14. September 2000
• Englisch
• Abmessung: 235mm x 155mm x 17mm
• Gewicht: 469g
• ISBN-13: 9780387951089
• ISBN-10: 0387951083
• Artikelnr.: 24995577

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.