Randomized Response: Theory and Techniques

Randomized Response: Theory and Techniques

Randomized Response: Theory and Techniques

Randomized Response: Theory and Techniques

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Overview

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.


Product Details

ISBN-13: 9781351419833
Publisher: CRC Press
Publication date: 10/28/2020
Sold by: Barnes & Noble
Format: eBook
Pages: 184
File size: 8 MB

About the Author

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.

Table of Contents

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 py•

The Case of the Unknown py

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 D

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