This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 416
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #307525
Title: Minimum Hellinger Distance Estimation for a Semiparametric Mixture Model with Two-Sample Censored Data
Author(s): Yayuan Zhu*+ and Xuewen Lu and Jingjing Wu
Companies: University of Calgary and University of Calgary and University of Calgary
Address: Math Science Building , Calgary, AB, T2N 1N4 , Canada
Keywords: Right Censored Data ; Minimum Hellinger Distance Estimation ; Two-Sample Semiparametric Mixture Model ; Kernel Estimation ; Robustness
Abstract:

Efficiency and robustness are two important concerns on point estimation. Unfortunately, it was widely accepted that there existed a contradiction between pursuing efficiency and robustness simultaneously. That means an efficient estimator sometimes could not be a robust one and vice versa. For parametric model with complete data, the Minimum Hellinger Distance (MHD) estimation introduced by Beran (1977) has been showed it can reconcile this pair of contradiction. Because the event times of interest in biostatistics, actuarial science or economics are often subject to censoring. Now, this paper aims to extend the MHD estimation to a two-sample semiparametric model with right censored data. The performances of this proposed estimator will be showed via simulation and compared with those of MLE. Finally, this method will be illustrated by analyzing a real data set about melanoma.


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