Poisson item count techniques with noncompliance

The Poisson item count technique (PICT) is a survey method that was recently developed to elicit respondents' truthful answers to sensitive questions. It simplifies the well-known item count technique (ICT) by replacing a list of independent innocuous questions in known proportions with a single innocuous counting question. However, ICT and PICT both rely on the strong "no design effect assumption" (ie, respondents give the same answers to the innocuous items regardless of the absence or presence of the sensitive item in the list) and "no liar" (ie, all respondents give truthful answers) assumptions. To address the problem of self-protective behavior and provide more reliable analyses, we introduced a noncompliance parameter into the existing PICT. Based on the survey design of PICT, we considered more practical model assumptions and developed the corresponding statistical inferences. Simulation studies were conducted to evaluate the performance of our method. Finally, a real example of automobile insurance fraud was used to demonstrate our method.

[1]  Guido Dedene,et al.  A Comparison of State-of-The-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection , 2002 .

[2]  Harry Joe,et al.  Generalized Poisson Distribution: the Property of Mixture of Poisson and Comparison with Negative Binomial Distribution , 2005, Biometrical journal. Biometrische Zeitschrift.

[3]  Yibo Wang,et al.  Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud , 2018, Decis. Support Syst..

[4]  K. Liang,et al.  Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions , 1987 .

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

[6]  Kosuke Imai,et al.  Statistical Analysis of List Experiments , 2012, Political Analysis.

[7]  K. Rasinski,et al.  Methods of data collection, perceptions of risks and losses, and motivation to give truthful answers to sensitive survey questions , 1999 .

[8]  Kosuke Imai,et al.  Multivariate Regression Analysis for the Item Count Technique , 2011 .

[9]  Ziding Feng,et al.  Statistical Inference Using Maximum Likelihood Estimation and the Generalized Likelihood Ratio when the True Parameter is on the Boundary of the Parameter Space , 1992 .

[10]  Kosuke Imai,et al.  List Experiments with Measurement Error , 2019, Political Analysis.

[11]  John Hinde,et al.  Score tests for zero-inflated Poisson models , 2002 .

[12]  G. Tian,et al.  Two new models for survey sampling with sensitive characteristic: design and analysis , 2008 .

[13]  Kosuke Imai,et al.  Design and Analysis of the Randomized Response Technique , 2015 .

[14]  Man-Lai Tang,et al.  Non-randomized response model for sensitive survey with noncompliance , 2016, Statistical methods in medical research.

[15]  Man-Lai Tang,et al.  Poisson and negative binomial item count techniques for surveys with sensitive question , 2017, Statistical methods in medical research.

[16]  Ulf Böckenholt,et al.  Log-Linear Randomized-Response Models Taking Self-Protective Response Behavior Into Account , 2007 .