JSM 2005 - Toronto

Abstract #303592

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Activity Number: 127
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #303592
Title: Minimum Disparity Estimation in Ranked Set Sampling
Author(s): Roxana Alexandridis*+ and Omer Ozturk
Companies: The Ohio State University and The Ohio State University
Address: 125 W Dodridge Street Apt 311, Columbus, OH, 43202, United States
Keywords: Bias ; Mean square error ; Hellinger distance ; Imperfect ranking ; Robustness ; Minimum distance estimation
Abstract:

One of the main concerns in ranked set sampling is that the efficiency of the statistical procedure diminishes with the decrease in ranking quality. To address this concern, we develop robust statistical inference based on minimum disparity measures for discrete distributions. We show that all minimum disparity estimators are asymptotically efficient under perfect ranking. We also show that there exists an estimator within this class that produces substantially smaller bias than the bias of the maximum likelihood estimator under imperfect ranking. We provide examples for Bernoulli and Poisson models to evaluate the performance of the proposed procedure. For finite samples, we present simulation results.

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