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Data Gathering, Analysis and Protection of Privacy Through Randomized Response Techniques: Qualitative and Quantitative Human Traits, Volume 34 [Kietas viršelis]

Volume editor (Indian Statistical Institute, Kolkata, India), Volume editor (University of Hyderabad Campus, India), Volume editor (University of Cyprus, Nicosia, Cyprus)
  • Formatas: Hardback, 544 pages, aukštis x plotis: 229x152 mm, weight: 700 g
  • Serija: Handbook of Statistics
  • Išleidimo metai: 13-Apr-2016
  • Leidėjas: North-Holland
  • ISBN-10: 044463570X
  • ISBN-13: 9780444635709
Kitos knygos pagal šią temą:
Data Gathering, Analysis and Protection of Privacy Through Randomized Response Techniques: Qualitative and Quantitative Human Traits, Volume 34Didesnis paveikslėlis
  • Formatas: Hardback, 544 pages, aukštis x plotis: 229x152 mm, weight: 700 g
  • Serija: Handbook of Statistics
  • Išleidimo metai: 13-Apr-2016
  • Leidėjas: North-Holland
  • ISBN-10: 044463570X
  • ISBN-13: 9780444635709
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780444635709.html
Raktažodžiai:

Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits tackles how to gather and analyze data relating to stigmatizing human traits. S.L. Warner invented RRT and published it in JASA, 1965. In the 50 years since, the subject has grown tremendously, with continued growth. This book comprehensively consolidates the literature to commemorate the inception of RR.

  • Brings together all relevant aspects of randomized response and indirect questioning
  • Tackles how to gather and analyze data relating to stigmatizing human traits
  • Gives an encyclopedic coverage of the topic
  • Covers recent developments and extrapolates to future trends

Daugiau informacijos

This volume of the Handbook of Statistics brings together all relevant aspects of randomized response and indirect questioning, expertly tackling how to gather and analyze data relating to stigmatizing human traits
Contributors xv
Preface xvii
1 Review of Certain Recent Advances in Randomized Response Techniques 1(12)
T.J. Rao
C.R. Rao
1 Introduction
1(1)
2 Warner's and Related Techniques
2(1)
3 Cryptographic RRT
3(1)
4 Reverse RRT
4(1)
5 Certain Recent Theoretical and Practical Results
5(3)
5.1 Unified Theory
5(1)
5.2 Stratification and RRT
6(1)
5.3 Cramer-Rao Lower Bound
7(1)
5.4 Game Theory and RRT
7(1)
5.5 Smart Phones and RRT
7(1)
5.6 Alternatives to RRT
8(1)
5.7 Meta Analysis
8(1)
6 Epilogue
8(1)
Acknowledgment
9(1)
References
9(4)
2 The Background and Genesis of Randomized Response Techniques 13(4)
A. Chaudhuri
3 How Randomized Response Techniques Need not Be Confined to Simple Random Sampling but Liberally Applicable to General Sampling Schemes 17(12)
A. Chaudhuri
1 Introduction
17(2)
2 Two Prominent RR Devices Revised for General Applications
19(3)
2.1 Warner Stanley's (1965) Device
19(1)
2.2 Simmon's RR Device Revised
20(2)
3 Quantitative RRs
22(1)
4 Protection of Privacy
23(1)
4.1 When a Characteristic Is Qualitative and SRSWR Is Allowed
23(1)
4.2 When a General Sampling Design Is Allowed to Cover a Qualitative Characteristic
23(1)
4.3 Protection of Privacy Covering Quantitative Variables
24(1)
5 Optional RR Techniques
24(2)
References
26(3)
4 The Classical Randomized Response Techniques: Reading Warner (1965) and Greenberg et al. (1969) 50 Years Later 29(14)
T.C. Christofides
1 Introduction
29(1)
2 Warner's Randomized Response Technique
30(3)
3 The Unrelated Question Model
33(4)
4 Reading Warner (1965) and Greenberg et al. (1969) 50 Years Later
37(3)
5 Epilogue
40(1)
References
40(3)
5 On the Estimation of Correlation Coefficient Using Scrambled Responses 43(48)
S. Singh
1 Introduction
43(2)
2 Two Scrambling Variable Randomized Response Technique
45(1)
3 Scrambling Variables Are Dependent
46(2)
4 Estimation of the Correlation Coefficient pxy
48(6)
5 Bias and Mean Squared Error of rxy
54(4)
6 Scrambling Variables Are Independent
58(1)
7 Bias and Mean Square Error of r1
59(2)
8 Single Scrambling Variable Randomized Response Technique
61(2)
9 Bias and Mean Squared Error of r2
63(4)
10 Correlation Between Sensitive and Nonsensitive Variable
67(2)
11 Bias and Mean Square Error of r3
69(1)
12 Simulation Study
69(13)
Acknowledgments
82(1)
Appendix
83(6)
References
89(2)
6 Admissible and Optimal Estimation in Finite Population Sampling Under Randomized Response Models 91(14)
S. Sengupta
1 Introduction
91(1)
2 Notations and Preliminaries
92(4)
3 Estimation Based on Single RR
96(3)
3.1 Nonexistence of a Best Estimator
96(1)
3.2 Admissibility Results
96(2)
3.3 Optimality Results
98(1)
4 Estimation Based on Independent Multiple Responses
99(2)
4.1 Nonexistence of a Best Estimator
100(1)
4.2 Admissibility Results
100(1)
4.3 Optimality Results
101(1)
5 Concluding Remarks
101(1)
References
102(3)
7 A Mixture of True and Randomized Responses in the Estimation of the Number of People Having a Certain Attribute 105(14)
A. Quatember
1 Introduction
105(3)
2 A General RR Technique for the Estimation of Group Size
108(1)
3 Combining True and Randomized Responses
109(5)
4 A Vivid Illustration of This Strategy Including True and Masked Responses
114(3)
References
117(2)
8 Estimation of Complex Population Parameters Under the Randomized Response Theory 119(14)
L. Barabesi
G. Diana
P.F. Perri
1 Introduction
119(2)
2 Foundations of Functional Linearization
121(2)
3 Functional Linearization with the RR Technique
123(2)
4 Some Simple Examples
125(1)
5 Further Examples with Some Inequality Indices
126(3)
6 Final Remarks
129(1)
Acknowledgments
129(1)
References
130(3)
9 An Efficient Randomized Response Model Using Two Decks of Cards Under Simple and Stratified Random Sampling 133(22)
S. Abdelfatah
R. Mazloum
1 Introduction
133(3)
2 Efficient Randomized Response Model Under Simple Random Sampling
136(5)
2.1 The Proposed Model
136(3)
2.2 Efficiency Comparison
139(2)
3 Simulation Study
141(1)
4 Efficient Randomized Response Model Under Stratified Random Sampling
142(6)
4.1 The Proposed Stratified Model
142(5)
4.2 Efficiency Comparison
147(1)
5 Double Sampling for the Proposed Stratified Model
148(5)
6 Conclusion
153(1)
References
153(2)
10 Software for Randomized Response Techniques 155(14)
M. Rueda
B. Cobo
A. Arcos
R. Arnab
1 Introduction
155(1)
2 Software for Helping to Conduct a Survey with RR
156(2)
3 Software for the Estimation with Data Obtained Using RR Techniques
158(4)
4 Summary
162(2)
Acknowledgments
164(1)
References
164(5)
11 Poststratification Based on the Choice of Use of a Quantitative Randomization Device 169(22)
O. Odumade
R. Arnab
S. Singh
1 Introduction
169(1)
2 Poststratification Based on the Choice of a Quantitative Randomization Device
170(3)
3 Relative Efficiency
173(1)
Appendix
174(15)
References
189(2)
12 Variance Estimation in Randomized Response Surveys 191(18)
A.K. Adhikary
1 Introduction
191(3)
2 Variance of Horvitz-Thompson (1952) Estimator
194(3)
3 Variance of Hansen-Hurwitz (1943) Estimator
197(3)
4 Variance of Raj's (1956) Ordered Estimator
200(2)
5 Variance of Murthy's (1957) Unordered Estimator
202(2)
6 Variance of Ratio Estimator Based on Lahiri (1951), Midzuno (1952), and Sen's (1953) Sampling Scheme
204(2)
7 Variance of Hartley-Ross (1954) Unbiased Ratio-Type Estimator
206(1)
References
207(2)
13 Behavior of Some Scrambled Randomized Response Models Under Simple Random Sampling, Ranked Set Sampling and Rao-Hartley-Cochran Designs 209(12)
C.N. Bouza-Herrera
1 Introduction
209(1)
2 Scrambling Procedures
210(2)
3 Behavior Under RHC Unequal Probability Model
212(2)
4 Behavior Under RSS
214(5)
5 Conclusions
219(1)
Acknowledgments
220(1)
References
220(1)
14 Estimation of a Finite Population Variance Under Linear Models for Randomized Response Designs 221(12)
P. Mukhopadhyay
1 Introduction
221(3)
2 Optimal Estimation of V
224(4)
3 UMVU Estimation
228(2)
References
230(3)
15 Randomized Response and New Thoughts on Politz-Simmons Technique 233(20)
T.J. Rao
J. Sarkar
B.K. Sinha
1 Introduction
233(1)
2 Not-at-Home's
234(12)
2.1 Randomized Response Hartley-Politz-Simmons FRR-HPS] Technique
234(2)
2.2 A New RR-HPS Technique
236(10)
Acknowledgments
246(1)
Appendix
246(5)
References
251(2)
16 Optional Randomized Response: A Critical Review 253(20)
R. Arnab
M. Rueda
1 Introduction
253(3)
1.1 Warner's Technique: The Pioneering Method
254(1)
1.2 Ericksson's Technique
255(1)
1.3 A More General Model
256(1)
2 General Method of Estimation
256(2)
3 Optional Randomized Response Techniques
258(9)
3.1 Full ORT
258(6)
3.2 Partial ORT
264(3)
4 Efficiency of the ORT
267(1)
5 Conclusion
268(1)
References
269(4)
17 A Concise Theory of Randomized Response Techniques for Privacy and Confidentiality Protection 273(14)
T.K. Nayak
S.A. Adeshiyan
C. Zhang
1 Introduction
273(2)
2 Vital Attributes of Randomization Experiments
275(3)
3 Statistical Estimation for Fixed P
278(2)
4 Estimation Under Invariant Post-randomization
280(2)
5 Assessing Privacy and Confidentiality Protection
282(2)
6 Discussion
284(1)
Acknowledgments
284(1)
References
285(2)
18 A Review of Regression Procedures for Randomized Response Data, Including Univariate and Multivariate Logistic Regression, the Proportional Odds Model and Item Response Model, and Self-Protective Responses 287(30)
M.J.L.F. Cruyff
U. Bockenholt
P.G.M. van der Heijden
L.E. Frank
1 Introduction
288(1)
2 Univariate and Multivariate RR Data, No Explanatory Variables
289(3)
2.1 Theory
289(2)
2.2 Estimation
291(1)
3 Logistic Regression of Univariate RR Data
292(5)
3.1 Theory
292(1)
3.2 Estimation
293(1)
3.3 Extensions
294(3)
4 Extensions of Regression Approaches to Multivariate RR Data
297(7)
4.1 The Multivariate Logistic Regression Model Proposed by Glonek and McCullagh
297(4)
4.2 Proportional Odds Model
301(3)
5 Models Including Self-Protective Responses
304(9)
5.1 Item Response Models
304(1)
5.2 Self-Protective Responses
305(1)
5.3 Example
306(5)
5.4 Remaining Issues
311(2)
References
313(4)
19 Eliciting Information on Sensitive Features: Block Total Response Technique and Related Inference 317(14)
K. Nandy
M. Marcovitz
B.K. Sinha
1 Randomized Response Technique
317(1)
2 Block Total Response Technique
318(2)
3 SBTRM : Use of BIBD and Complimentary BIBD
320(1)
4 Relative Comparison of BIBD-Based SBTRMs
320(1)
5 Deriving the EB Estimators
321(3)
6 Illustrative Example
324(2)
7 Concluding Remarks
326(1)
Acknowledgment
326(1)
Appendix
326(2)
A.1 Illustrative Examples
327(1)
References
328(3)
20 Optional Randomized Response Revisited 331(10)
R. Mukerjee
1 Introduction
331(1)
2 Early Work
332(2)
3 Scrambled Response
334(3)
4 General Sampling Designs
337(1)
5 Concluding Remarks
338(1)
Acknowledgments
339(1)
References
339(2)
21 Measures of Respondent Privacy in Randomized Response Surveys 341(12)
M. Bose
1 Introduction
341(2)
2 Qualitative Stigmatizing Variable
343(3)
3 Quantitative Stigmatizing Variable
346(3)
3.1 Continuous Stigmatizing Variable
346(1)
3.2 Discrete Stigmatizing Variable
347(2)
4 Concluding Remarks
349(1)
References
350(3)
22 Cramer-Rao Lower Bounds of Variance for Estimating Two Proportions and Their Overlap by Using Two Decks of Cards 353(34)
C.S. Lee
S.A. Sedory
S. Singh
1 Introduction
353(4)
1.1 Simple Model
354(2)
1.2 Crossed Model
356(1)
2 Cramer-Rao Lower Bounds of Variances for the Simple Model
357(2)
3 Cramer-Rao Lower Bounds of Variances for the Crossed Model
359(2)
4 Comparison of the Variances and Lower Bounds
361(3)
5 Unique Estimates
364(2)
6 Range Restricted Maximum Likelihood Estimates
366(4)
Acknowledgment
370(1)
Appendix A
371(2)
Appendix B Codes Used in Simulation Studies
373(12)
References
385(2)
23 Estimating a Finite Population Proportion Bearing a Sensitive Attribute from a Single Probability Sample by Item Count Technique 387(18)
P. Shaw
1 Introduction
388(1)
2 Item Count Technique Using a Single Sample
389(4)
3 An Alternative Estimator of VP (ΘA)
393(2)
4 Numerical Presentation
395(6)
5 Conclusion
401(2)
Acknowledgments
403(1)
References
403(2)
24 Surveying a Varying Probability Adaptive Sample to Estimate Cost of Hospital Treatments of Sensitive Diseases by RR Data Gathering 405(8)
S. Pal
S. Roy
1 Introduction
405(1)
2 Formulation of Problem
406(1)
3 RR Surveys
407(1)
4 Adaptive Cluster Sampling
407(1)
4.1 Network
408(1)
5 Revised Adaptive Randomized Response Surveys
408(2)
6 Simulation Study
410(2)
7 Concluding Remarks
412(1)
References
412(1)
25 Estimation of Means of Two Rare Sensitive Characteristics: Cramer-Rao Lower Bound of Variances 413(14)
S.C. Su
C.S. Lee
S.A. Sedory
S. Singh
1 Introduction
413(3)
2 Estimation of Two Rare Sensitive Attributes
416(3)
3 Proposed Randomized Response Model for Two Rare Sensitive Attributes
419(3)
4 Relative Efficiency
422(2)
Acknowledgments
424(1)
Appendix
424(1)
References
425(2)
26 Estimating Sensitive Population Proportion by Generating Randomized Response Following Direct and Inverse Hypergeometric Distribution 427(16)
K. Dihidar
1 Introduction
427(2)
2 Generating RR by Hypergeometric Distribution
429(1)
3 Generating RR by Negative Hypergeometric Distribution
430(2)
4 Comparative Efficiencies of Inverse Hypergeometric vs Direct Hypergeometric RR Generation for Different Sampling Schemes
432(5)
4.1 SRSWR in n Draws
433(1)
4.2 SRSWOR in n Draws
433(1)
4.3 PPSWR in n Draws
434(1)
4.4 Rao, Hartley, and Cochran's Sampling of Size n
434(1)
4.5 Midzuno's (1952) Sampling of n Persons
435(1)
4.6 Comparison of the Efficiencies
436(1)
5 Numerical Illustration Showing Relative Performances by Simulation
437(3)
6 Concluding Remarks
440(1)
Acknowledgment
440(1)
References
440(3)
27 Incredibly Efficient Use of a Negative Hypergeometric Distribution in Randomized Response Techniques 443(28)
M.L. Johnson
S.A. Sedory
S. Singh
1 Introduction
443(4)
2 Singh and Sedory Randomization Device
447(5)
3 Proposed Incredibly Efficient Randomization Device
452(5)
4 Efficiency Comparison
457(2)
5 Limiting Case with Four Decks of Cards
459(4)
6 Relative Efficiency of the Limiting Case
463(3)
Acknowledgments
466(1)
Appendix
466(3)
References
469(2)
28 Comparison of Different Imputing Methods for Scrambled Responses 471(26)
C. Mohamed
S.A. Sedory
S. Singh
1 Introduction
471(3)
2 Ratio Method of Imputing Scrambled Responses
474(1)
3 Regression Method of Imputing Scrambled Responses
475(1)
4 Imputing Scrambled Responses Using Higher Order Moments of An Auxiliary Variable
476(1)
5 Some Useful Results
477(1)
6 Properties of Different Estimators
478(4)
7 Application to a Real Data Set
482(11)
Acknowledgments
493(1)
References
493(4)
29 On an Indirect Response Model 497(18)
V.R. Padmawar
1 Preliminaries
497(2)
2 Introducing the Model
499(13)
2.1 Method of Moments
500(4)
2.2 Maximum Likelihood or Pseudolikelihood Method
504(5)
2.3 Bayesian Framework
509(3)
Acknowledgments
512(1)
References
513(2)
Index 515
Professor Arijit Chaudhuri (1940) has a Ph.D, M.A. and B.A. (Statistics) from Calcutta University(CU), was Lecturer in Statistics in Presidency College (1963-68) and CU (1968-77) and Associate Professor in Indian Statistical Institute (ISI) (1977-1981), a Professor at ISI (1982-2002) and Honorary Visiting Professor there since then including a position as CSIR Emeritus Scientist at ISI (2002-2005). He still regularly teaches B.Stat and M.Stat courses at ISI.

Internationally he was a Post-Doc fellow at Sydney University (1973-75), a Visiting Professor (1989-1990) at Virginia Tech, Nebraska-Lincoln University (1997), Delft University (1985). Ha also worked on scientific assignments in Ottawa, Waterloo, Mannheim, Utrecht, Lund, Umea, Stockholm, Southampton, Jerusalem, Cyprus, Havana, Istanbul, Chiba, Durban (universities & Statistical Offices) intermittently over 1979-2009.

He has successfully guided 10 Ph.D. students.

He has published 10 books/monographs:

Network & adaptive Sampling (2014) CRC Press Modern Survey Sampling (2014) CRC Press Indirect Questioning in sample surveys (with TC Christofides)(2013), Springer Verlag Randomized Response and Indirect Questioningtechniques in surveys (2011),CRC Press Essentials of Survey Sampling (2010), Prentice Hall of India Survey Sampling Theory & Methods (with H.Stenger) ( 1st edition, 1992,Marcel Dekker) 2nd revised and enhanced edition, 2005, CRC Press Randomized response theory & Techniques (1988) (with Rahul Mukerjee),Marcel Dekker Unified Theory and Strategies of Survey Sampling (1988)(with late JWE Vos), North Holland (Elsevier) Developing small Domain Statistics:Modeling in survey sampling; (2012) , e-Book, LAP, Saarbrucken, Germany





In addition he has has published 125 peer-reviewed papers alone and jointly in journals including Biometrika, Int. Stat. Rev, Metrika, Stat. Neerlandica, Aust. J.Stat., JSPI, Comm. Stat (Theo. Meth and Comp. Simul), Sankhya, Cal. Stat. Assoc. Bull., J. Ind. Soc. Agri. Stat.

Tasos C. Christofides is a Professor in the Department of Mathematics and Statistics of the University of Cyprus. He completed his Ph.D. in Mathematical Sciences at Johns Hopkins University in 1987, under the supervision of Professor Robert Serfling. From 1987 until 1991 he was an Assistant Professor in the Department of Mathematical Sciences at the State University of New York at Binghamton. In 1991 he joined the newly founded University of Cyprus and the Department of Mathematics and Statistics, becoming its first Chairman. His areas of expertise include indirect questioning techniques and probability inequalities. He is co-author (with Professor Arijit Chaudhuri) of a recent book on Indirect Questioning Techniques in Sample Surveys (Springer, Heidelberg 2013) and author of a number of papers on randomized response and indirect questioning techniques in general. He serves on the editorial board of various journals of probability and statistics. He has participated in a number of research projects and has provided consulting services for various organizations including the Statistical Service of Cyprus. He serves on various national and international scientific and expert committees, among them, the European Statistical Advisory Committee. book Ancient Inhabitants of Jebel Moya” published by the Cambridge Press under the joint authorship of Rao and two anthropologists. On the basis of work done at CU during the two year period, 1946-1948, Rao earned a Ph.D. degree and a few years later Sc.D. degree of CU and the rare honor of life fellowship of Kings College, Cambridge.

He retired from ISI in 1980 at the mandatory age of 60 after working for 40 years during which period he developed ISI as an international center for statistical education and research. He also took an active part in establishing state statistical bureaus to collect local statistics and transmitting them to Central Statistical Organization in New Delhi. Rao played a pivitol role in launching undergraduate and postgraduate courses at ISI. He is the author of 475 research publications and several breakthrough papers contributing to statistical theory and methodology for applications to problems in all areas of human endeavor. There are a number of classical statistical terms named after him, the most popular of which are Cramer-Rao inequality, Rao-Blackwellization, Raos Orthogonal arrays used in quality control, Raos score test, Raos Quadratic Entropy used in ecological work, Raos metric and distance which are incorporated in most statistical books.

He is the author of 10 books, of which two important books are, Linear Statistical Inference which is translated into German, Russian, Czec, Polish and Japanese languages,and Statistics and Truth which is translated into, French, German, Japanese, Mainland Chinese, Taiwan Chinese, Turkish and Korean languages.

He directed the research work of 50 students for the Ph.D. degrees who in turn produced 500 Ph.D.s. Rao received 38 hon. Doctorate degree from universities in 19 countries spanning 6 continents. He received the highest awards in statistics in USA,UK and India: National Medal of Science awarded by the president of USA, Indian National Medal of Science awarded by the Prime Minister of India and the Guy Medal in Gold awarded by the Royal Statistical Society, UK. Rao was a recipient of the first batch of Bhatnagar awards in 1959 for mathematical sciences and and numerous medals in India and abroad from Science Academies. He is a Fellow of Royal Society (FRS),UK, and member of National Academy of Sciences, USA, Lithuania and Europe. In his honor a research Institute named as CRRAO ADVANCED INSTITUTE OF MATHEMATICS, STATISTICS AND COMPUTER SCIENCE was established in the campus of Hyderabad University.