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Abstract Details

Activity Number: 407
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #304573
Title: Optimal Confidence Sets for Parameters in Discrete Distributions
Author(s): Josh Habiger*+ and Melinda McCann and Joshua Tebbs
Companies: Oklahoma State University and Oklahoma State University and University of South Carolina
Address: Department of Statistics, Stillwater, OK, 74078-1056, United States
Keywords: Discrete Data ; Coverage Probability ; Expected Length ; Optimal ; Confidence Interval

In the analysis of discrete data, the coverage probability and expected length of an interval estimator depend on the unknown parameter of interest. Many have therefore suggested that ``good'' interval estimators should have mean coverage probability near the nominal level and small mean expected length, where the mean is taken over all possible values of the parameter. This paper uses these criteria to precisely define an optimal interval estimator and finds it. We allow for the mean to be taken with respect to any user-specified weight function, thereby facilitating a flexible definition of optimality.

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