Variable selection for bivariate interval-censored failure time data under linear transformation models

Author:

Liu Rong1,Du Mingyue1,Sun Jianguo2

Affiliation:

1. Center for Applied Statistical Research, School of Mathematics , Jilin University , Changchun 130012 , China

2. Department of Statistics , University of Missouri , Columbia , MO , 65211 , USA

Abstract

Abstract Variable selection is needed and performed in almost every field and a large literature on it has been established, especially under the context of linear models or for complete data. Many authors have also investigated the variable selection problem for incomplete data such as right-censored failure time data. In this paper, we discuss variable selection when one faces bivariate interval-censored failure time data arising from a linear transformation model, for which it does not seem to exist an established procedure. For the problem, a penalized maximum likelihood approach is proposed and in particular, a novel Poisson-based EM algorithm is developed for the implementation. The oracle property of the proposed method is established, and the numerical studies suggest that the method works well for practical situations.

Publisher

Walter de Gruyter GmbH

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

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