TY - JOUR
T1 - Variable selection for multivariate failure time data
AU - Cai, Jianwen
AU - Fan, Jianqing
AU - Li, Runze
AU - Zhou, Haibo
N1 - Funding Information:
This research was supported by grants from the U.S. National Institutes of Health and National Sciences Foundation. Fan’s research is also supported by a Research Grants Council grant at the Chinese University of Hong Kong. The authors thank the editor and referees for their helpful comments which greatly improved the paper.
PY - 2005/6
Y1 - 2005/6
N2 - 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 Newton-Raphson 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.
AB - 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 Newton-Raphson 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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U2 - 10.1093/biomet/92.2.303
DO - 10.1093/biomet/92.2.303
M3 - Article
AN - SCOPUS:21644468678
SN - 0006-3444
VL - 92
SP - 303
EP - 316
JO - Biometrika
JF - Biometrika
IS - 2
ER -