Variable selection for multivariate failure time data

Jianwen Cai Jianqing Fan Runze Li Haibo Zhou

Statistics Theory and Methods mathscidoc:1912.43305

Biometrika, 92, (2), 303-316, 2005.6
In this paper, we propose a penalised pseudo-partial likelihood method for variable selection with multivariate failure time data with a growing number of regression coefficients. Under certain regularity conditions, we show the consistency and asymptotic normality of the penalised likelihood estimators. We further demonstrate that, for certain penalty functions with proper choices of regularisation parameters, the resulting estimator can correctly identify the true model, as if it were known in advance. Based on a simple approximation of the penalty function, the proposed method can be easily carried out with the NewtonRaphson algorithm. We conduct extensive Monte Carlo simulation studies to assess the finite sample performance of the proposed procedures. We illustrate the proposed method by analysing a dataset from the Framingham Heart Study.
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  title={Variable selection for multivariate failure time data},
  author={Jianwen Cai, Jianqing Fan, Runze Li, and Haibo Zhou},
Jianwen Cai, Jianqing Fan, Runze Li, and Haibo Zhou. Variable selection for multivariate failure time data. 2005. Vol. 92. In Biometrika. pp.303-316.
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