JSM 2005 - Toronto

Abstract #302980

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 65
Type: Contributed
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: General Methodology
Abstract - #302980
Title: Robust and Efficient Estimation under Data Grouping
Author(s): Nan Lin*+ and Xuming He
Companies: Washington University in St. Louis and University of Illinois, Urbana Champaign
Address: 1 Brookings Drive, Saint Louis, MO, 63130, United States
Keywords: Hellinger distance ; Robustness ; Asymptotic normality ; Grouped data ; First-order approximation
Abstract:

The minimum Hellinger distance estimator (MHDE) is known to have the desirable properties in robustness and efficiency. We propose an approximate MHDE (AMHDE) by adapting to grouped data from a continuous distribution. The AMHDE is easier to compute for either the continuous or grouped data. Given certain conditions on the model distribution and reasonable grouping rules, the AMHDE is shown to be consistent and asymptotically normal. Furthermore, it is robust and can be asymptotically as efficient as the maximum likelihood estimator. The merit of AMHDE is demonstrated through simulation studies and real-data examples.

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