Warner in 1965 introduced randomized response, and since then many extensions and improvements to the Warner model have been done. In this study a randomized response model applicable to continuous data that considers a mixture of two normal distributions is considered and analyzed. This includes a study of the efficiency, an estimation of some unknown parameters and a discussion of contaminated data issues and an application of this method to the problem of estimating Oakland University student income is presented and discussed. Also, this study includes inference for two or more populations of the same structure as the randomized response model introduced.
The impact of this randomized response model on ranking and selection method is quantified for an indifference-zone procedure and a subset selection procedure. A study on how to choose the best population between k distinct populations using an indifference-zone procedure is presented and some tables for the required sample size needed to have a probability of correct selection higher than some specified value in the preference zone for the randomized response model considered are provided. An application of the subset selection procedure on the considered randomized response model is discussed. The subset selection study is provided for 2 configurations, the slippage configuration and the equi-spaced configuration, and tables are provided for both configurations.
Finally, a discussion on the use of the data obtained from the Bayesian Improved Surname and Geocoding analysis (BISG) tool in hypothesis testing for disparity between different populations. Two approaches are provided on how to use the information arising from the BISG.
|Advisor:||McDonald, Gary C.|
|Commitee:||Cahlon, Baruch, Jeffrey, Alden, Khattree, Ravindra, Perla, Subbaiah|
|School Location:||United States -- Michigan|
|Source:||DAI-B 78/11(E), Dissertation Abstracts International|
|Subjects:||Applied Mathematics, Statistics|
|Keywords:||Bisg, Indifference zone, Randomized response, Ranking and selection, Subset selection, Survey|
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