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Survey Sampling.

Bibliographic Details
Main Author: Chaudhuri, Arijit.
Format: eBook
Language:English
Published: Milton : CRC Press LLC, 2018.
Subjects:
Electronic books.
Online Access:View fulltext via EzAccess
Table of Contents:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Dedication
  • Table of Contents
  • Acknowledgment
  • About the author
  • Preface
  • My plea for this composition and my target readership
  • Introduction
  • Chapter 1: Certain Essential Preliminaries
  • 1.0 Summary
  • 1.1 Concepts of Population, Sample, Survey, Census, Sampling: Design and Schemes, Estimator and Strategy
  • 1.2 Properties of Estimators and Strategies
  • Different Inferential Approaches: Design-Based, Predictive, Super-Population Model-Based, Model-Assisted and Bayesian
  • 1.3 Sufficiency, Minimal Sufficiency and Bayesian Sufficiency
  • 1.4 Classes of Estimators
  • 1.5 Godambe's and Basu's Theorems on Non-Existence of UMV Estimators
  • 1.6 Hanurav's (1966) and Hege's (1965) Exceptions and Remedial Steps
  • Chapter 2: Further Essentials for Unstratified Uni-Stage Cases
  • 2.0 Summary
  • 2.1 Labels: Their Roles and Related Controversies
  • 2.2 Minimaxity
  • 2.3 Necessary and Sufficient Conditions for Existence of an Unbiased Estimator for a Total and of a Variance Estimator
  • 2.4 Determination of Sample-Size
  • 2.5 Varying Probability Sampling Methods and Associated Estimation Procedures
  • Chapter 3: More in Design-Based Sampling
  • 3.0 Summary
  • 3.1 Stratified Sampling and Other Sampling and Estimation Procedures
  • 3.2 Replicated Sampling and Its Applications
  • 3.3 Controlled Sampling
  • 3.4 Multi-Phase Sampling: Ratio and Regression Estimation
  • 3.5 Sampling on Successive Occasions and Panel Sampling
  • 3.6 Non-Sampling Error and Non-Response Error Problems: Weighting Adjustments and Imputation Techniques
  • Chapter 4: Super-Population Modeling and Its Various Uses
  • 4.0 Summary
  • 4.1 Super-Population Modeling
  • 4.2 Linearization Technique
  • 4.3 Small Area Estimation
  • 4.4 Jack-Knife
  • 4.5 Bootstrap in Finite Population Sampling.
  • 4.6 Balanced Repeated Replication (BRR)
  • 4.7 Kriging or Spatial Prediction
  • 4.8 Estimating Equations and Estimating Functions
  • 4.9 Basu's (1971) Circus Example
  • Chapter 5: Indirect Questioning in Sensitive Surveys
  • 5.0 Summary
  • 5.1 Randomized Response Techniques: General Sampling and Simple Random Sampling with Replacement
  • 5.2 A Few Indirect Questioning Techniques Other than RRT's
  • 5.3 Three More Indirect Questioning Techniques
  • Chapter 6: Adaptive and Network Sampling
  • 6.0 Summary
  • 6.1 Adaptive Sampling
  • 6.2 Network Sampling
  • 6.3 A Live Problem and Application
  • Chapter 7: Inadequate and Multiple Frames
  • 7.0 Summary
  • 7.1 Sampling from Inadequate Frames
  • 7.2 Sampling from Multiple Frames
  • 7.3 Conditional Inference
  • Chapter 8: Analytic Studies
  • 8.0 Summary
  • 8.1 Analytic Studies, Tests of Goodness of Fit, Independence, Homogeneity, Regression and Categorical Analysis
  • Chapter 9: Case Studies
  • 9.0 Summary
  • 9.1 Case Studies
  • Chapter 10: Lessons and Exercises
  • 10.0 Summary
  • 10.1 Examples, Exercises and Riders with Complete and Hinted Solutions
  • Chapter 11: Reviews
  • 11.0 Summary
  • 11.1 Reviews of Various Sampling Schemes
  • Chapter 12: An Appraisal
  • 12.0 Epilogue. An Appraisal of the Past, the Current and the Future Possibilities
  • References
  • Index.

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