High breakdown point robust regression with censored data

• 成果类型: Article
• 署名作者: Salibian-Barrera, Matias; Yohai, Victor J.
• 署名单位: University of British Columbia; University of Buenos Aires
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053607000000794
• 发表日期: 2008
• 页码: 118-146
• 关键词: LINEAR-REGRESSION; squares regression; large sample; covariables; estimators

摘要

In this paper, we propose a class of high breakdown point estimators for the linear regression model when the response variable contains censored observations. These estimators are robust against high-leverage outliers and they generalize the LMS (least median of squares), S, MM and tau-estimators for linear regression. An important contribution of this paper is that we can define consistent estimators using a bounded loss function (or equivalently, a re-descending score function). Since the calculation of these estimators can be computationally costly, we propose an efficient algorithm to compute them. We illustrate their use on an example and present simulation studies that show that these estimators also have good finite sample properties.