Generalized geometric distribution of order k: A flexible choice to randomize the response

ABSTRACT This article focuses on the improvement of a well-celebrated randomized response technique of Kuk. A generalized randomized response technique is suggested. In particular, the generalized geometric distribution of order k is introduced as a randomization device for estimating the population proportion of a rare sensitive attribute. The proposed randomized response technique includes Singh and Grewal and Hussain et al. techniques as its special cases. Through numerical illustrations, it is established that the suggested technique is superior to the Kuk, Singh and Grewal, and Hussain et al. techniques. Flexibility of the proposed technique is also discussed.

[1]  J. Corcoran Modelling Extremal Events for Insurance and Finance , 2002 .

[2]  Longest runs in coin tossing , 1994 .

[3]  Anthony Y. C. Kuk,et al.  Asking sensitive questions indirectly , 1990 .

[4]  Claude E. Shannon,et al.  Communication theory of secrecy systems , 1949, Bell Syst. Tech. J..

[5]  Naurang Singh Mangat,et al.  An alternative randomized response procedure , 1990 .

[6]  A new randomized response device for sensitive characteristics: an application of the negative hypergeometic distribution , 2013 .

[7]  On the Probability of Pattern Matching in Nonaligned DNA Sequences: A Finite Markov Chain Imbedding Approach , 1999 .

[8]  Sarjinder Singh,et al.  A new randomized response model , 2006 .

[9]  Horng-Jinh Chang,et al.  Estimation of proportion and sensitivity of a qualitative character , 2001 .

[10]  Run probabilities and the motion of a particle on a given path , 1986 .

[11]  T. Kirkwood,et al.  An accurate approximation to the distribution of the length of the longest matching word between two random DNA sequences. , 1990, Bulletin of mathematical biology.

[12]  Narayanaswamy Balakrishnan,et al.  Start‐up demonstration tests: models, methods and applications, with some unifications , 2014 .

[13]  Craig A. Stewart,et al.  Introduction to computational biology , 2005 .

[14]  J. J. A. Moors,et al.  Optimization of the Unrelated Question Randomized Response Model , 1971 .

[15]  N. S. Mangat,et al.  An Improved Randomized Response Strategy , 1994 .

[16]  M S Waterman,et al.  Poisson, compound Poisson and process approximations for testing statistical significance in sequence comparisons. , 1992, Bulletin of mathematical biology.

[17]  Michael R. Chernick,et al.  Runs and Scans With Applications , 2002, Technometrics.

[18]  Bernard G. Greenberg,et al.  The Two Alternate Questions Randomized Response Model for Human Surveys , 1973 .

[19]  P. Olmstead Runs determined in a sample by an arbitrary cut , 1958 .

[20]  Kenneth J. Kerpez Runlength codes from source codes , 1991, IEEE Trans. Inf. Theory.

[21]  Małgorzata Roos,et al.  Runs and Scans With Applications , 2001 .

[22]  Sarjinder Singh,et al.  Improved Bar-Lev, Bobovitch, and Boukai Randomized Response Models , 2009, Commun. Stat. Simul. Comput..

[23]  W. R. Simmons,et al.  The Unrelated Question Randomized Response Model: Theoretical Framework , 1969 .

[24]  Petros E. Maravelakis,et al.  Statistical Process Control using Shewhart Control Charts with Supplementary Runs Rules , 2007 .

[25]  Geometric Distribution as a Randomization Device: Implemented to the Kuk's Model , 2013 .

[26]  Tasos C. Christofides,et al.  A generalized randomized response technique , 2003 .

[27]  B. Arazi Handwriting Identification by Means of Run-Length Measurements , 1977 .

[28]  S L Warner,et al.  Randomized response: a survey technique for eliminating evasive answer bias. , 1965, Journal of the American Statistical Association.

[29]  M. Waterman Frequencies of restriction sites. , 1983, Nucleic acids research.

[30]  Andreas N. Philippou,et al.  A generalized geometric distribution and some of its properties , 1983 .

[31]  S. Bersimis,et al.  Run and Frequency Quota Rules in Process Monitoring and Acceptance Sampling , 2009 .

[32]  Zawar Hussain,et al.  On Using Negative Binomial Distribution as a Randomization Device in Sensitive Surveys , 2016, Commun. Stat. Simul. Comput..

[33]  S. Ross A Note on Optimal Stopping For Success Runs , 1975 .

[34]  George K. Karagiannidis,et al.  An Accurate Approximation to the Distribution of the Sum of Equally Correlated Nakagami-m Envelopes and Its Application in Equal Gain Diversity Receivers , 2009, 2009 IEEE International Conference on Communications.

[35]  V. Chvátal,et al.  Longest common subsequences of two random sequences , 1975, Advances in Applied Probability.

[36]  N. Starr How to Win a War if You Must: Optimal Stopping Based on Success Runs , 1972 .

[37]  Pier Francesco Perri,et al.  Modified randomized devices for Simmons' model , 2008, Model. Assist. Stat. Appl..

[38]  M. Sandri,et al.  A note on the comparison of some randomized response procedures , 2007 .

[39]  Michael S. Waterman,et al.  Critical Phenomena in Sequence Matching , 1985 .