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Simultaneous Estimation and Variable Selection for Cox-Aalen Transformation Models
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DOI：10.1007/s12561-026-09522-y.png)
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Abstract
En 中文
This paper discusses simultaneous estimation and variable selection for a class of Cox-Aalen transformation models, one of the most general types of models commonly used in regression analysis of failure time data. For the problem, we propose a penalized maximum likelihood estimation procedure with the use of the minimum information criterion (MIC). Unlike most of penalized methods, the proposed approach does not need the selection of the tuning parameter, which makes it both stable and efficient. For the implementation of the proposed method, an EM algorithm is developed and an extensive simulation study is conducted and suggests that it works well in practical situations. Finally, it is applied to a motivating breast cancer study.
Keywords:
Cox-Aalen transformation model
EM algorithm
Minimum information criterion
Variable selection
Journal
S
IF:
0.4
Papers:
22
Citations:
360