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

Author

Listed:
  • Liu Rong

    (Center for Applied Statistical Research, School of Mathematics, Jilin University, Changchun 130012, China)

  • Du Mingyue

    (Center for Applied Statistical Research, School of Mathematics, Jilin University, Changchun 130012, China)

  • Sun Jianguo

    (Department of Statistics, University of Missouri, Columbia, MO, 65211, USA)

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.

Suggested Citation

  • Liu Rong & Du Mingyue & Sun Jianguo, 2023. "Variable selection for bivariate interval-censored failure time data under linear transformation models," The International Journal of Biostatistics, De Gruyter, vol. 19(1), pages 61-79, May.
    • Handle: RePEc:bpj:ijbist:v:19:y:2023:i:1:p:61-79:n:7
    DOI: 10.1515/ijb-2021-0031
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