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• W3163761441 abstract "We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence component, then conduct group variable selection. The group bridge penalty is adopted for bi-level variable selection on both parts of the mixture cure models. Through simulation studies and real data analyses, we show that the proposed method can identify the important variables and groups that contribute to the cure proportion and the survival function for the uncured subjects, respectively." @default.
• W3163761441 created "2021-05-24" @default.
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• W3163761441 date "2021-05-17" @default.
• W3163761441 modified "2023-09-25" @default.
• W3163761441 title "Bi-level variable selection in semiparametric transformation mixture cure models for right-censored data" @default.
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• W3163761441 doi "https://doi.org/10.1080/03610918.2021.1926499" @default.
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