Offering a concise account of the most appropriate and efficient procedures for analyzing data from queries dealing with sensitive and confidential issues- including the first book-length treatment of infinite and finite population set-ups - this volume begins with the simplest problems, complete with their properties and solutions, and proceeds to incrementally more difficult topics. Randomized Response is mandatory reading for statisticians and biostatisticians, market researchers, operations researchers, pollsters, sociologists, political scientists, economists and advanced undergraduate and graduate students in these areas.
Foreword (Pranab Kumar Sen)
Preface
Acknowledgements
Introduction to Randomized Response: The Warner Model
Introduction: Why Randomized Response?
The Warner Model
Exercises
References
Appendix 1: Supplementary Remarks on the Warner Model
A1.1 Randomized Response Versus Direct Response
A1.2 Unbiased Estimation in the Warner Model
A1.3 Maximum Likelihood Estimation with the Warner Model
A1.4 Simple Random Sampling Without Replacement (SRSWOR) and the Warner Model
A1.5 Augmentation Modeling
Exercises
References
The Unrelated-Question Model
Introduction
The Case of Known πy•
The Case of the Unknown πy
Optimal Choice of Design Parameters
Comparison of the Warner Model and the Unrelated Question Model
Model with Two Unrelated Characters
Implicit Randomization
Exercises
References
Appendix 2: Supplementary Remarks on the Unrelated Question Model
A2.1 Unbiased and Maximum Likelihood Estimation
A2.2 SRSWOR with Simmons’ RRT
A2.3 Symmetry of Response
Exercises
References
Polychotomous Population and Multiattribute Situations
Introduction
Some Techniques for a Polychotomous Population
Use of Vector Response
Techniques for Multiattribute Situations
Exercises
References
Appendix 3 Supplementary Remarks on the Polychotomous and Multiattribute Models
A3.1 Augmentation Modeling
A3.2 Two-Stage Schemes
A3.3 Some Remarks
References
Techniques for Quantitative Characters
Introduction
The Unrelated-Question Model
Some Additional Techniques
Estimation of a Distribution Function
Applications of Hoeffding’s U Statistic and Von Mises’ Differentiable Statistical Functions
Exercises
References
Efficient Estimation and Protection of Privacy
Introduction
Dichotomous Population: "Yes-No" Response
General RR Models with Dichotomous Population
Polychotomous Models
Additional Generalities
References
Miscellaneous Topics on RR Techniques
A Bayesian Approach
More Lying Models
Randomized Response Surveys Allowing Options for Direct Responses
Some Allied Methods for Sensitive Characters
References
RR in a Finite Population Setting: A Unified Approach; Sampling with Varying Probabilities
Introduction
Linear Unbiased Estimations
Linear Estimation with RR Subject to Observational Errors
Optimality of General Unbiased Estimators
Modifications of Certain Popular Sampling Strategies in Open Surveys when Responses Are Randomized
References
Application of RRT and Concluding Remarks
References
Case Studies
A Survey of the Socioeconomics Conditions of College Students in Calcutta with Emphasis on Drug Habits
Randomized Response Survey with Sensitive Quantitative Characters: A Case Study
Randomized Response Technique to Determine Input in Crop Estimation
References
Appendix 4: Overview of Unified Theory of Direct Surveys
A4.1 Introduction and Notation
A4.2 Assortment of Leading Theoretical Results
References
Index
Biography
Arijit Chaudhuri is a Professor serving in the Computer Science Unit of Applied Statitsics Surveys and Computing Division at the Indian Statisitical Institute in Calcutta. Rahul Mukerjee is a faculty member in the Divison of Theoretical Statistics and Mathematics at the Indian Statistical Institure in Calcultta.
"Statisticians, operations researchers, pollsters, marketing researchers, sociologists, political scientists, economists, and graduate students will benefit from this wonderful book."
-Journal of Statistical Computation and Simulation
