2008/02/01 by Matías Salibian-Barrera, Víctor J. Yohai
Mathematics · #Advanced Statistical Methods and Models #Statistical Methods and Inference #Survey Sampling and Estimation Techniques #math.ST #msc:62F35 #msc:62J05 #stat.TH
paper · pdf · doi:10.1214/009053607000000794
published as Annals of Statistics 2008, Vol. 36, No. 1, 118-146 · Published in at http://dx.doi.org/10.1214/009053607000000794 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2008/02/01 · arxiv created 2008/03/12 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/04
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 τ-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 redescending 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.