Abstract #302118

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JSM 2003 Abstract #302118
Activity Number: 246
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #302118
Title: Linear Regression with Middle-Censored Data
Author(s): Vasudevan Mangalam*+ and Gopalan M. Nair and Yun Zhao
Companies: University of Brunei Darussalam and Curtin University of Technology and Curtin University of Technology
Address: Department of Mathematics UBD, Bandar Seri Begawan, , BE 1410, Brunei
Keywords: censoring ; middle censoring ; Buckley-James ; linear regression ; nonparametrics
Abstract:

Middle censoring is a kind of censoring in which the variable of interest becomes unobservable when it falls within a random interval of censorship. In this case, the exact values of some failure times are observed and other failure times are observed as intervals. Such data can often occur in clinical trials and lifetime studies. This is kind of censoring is a generalization of left censoring, right censoring, double censoring and interval censoring if the censorship intervals were allowed to be infinite. We consider the estimation of parameters in linear regression model with middle-censored data. No assumption is made about the error distribution, which is to be estimated nonparametrically. We propose a new estimator for the slope parameter by extending the Buckley-James method. To assess the performance of the new estimator, we conduct a comprehensive simulation study in which the least-squares estimator and the new estimator are computed and compared.

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