Variable selection in the additive rate model for recurrent event data
In this paper, we investigate the variable selection problem for recurrent event data under the additive rate model. According to the explicit estimator of the regression coefficients of the additive rate model, a loss function is constructed. It has a form similar to the ordinary least squares of a linear regression model up to a constant. We develop variable selection procedures by penalizing the loss function with the adaptive L1 penalty and smoothly clipped absolute derivation penalty, respectively. Under some mild regularity conditions, the oracle properties of both procedures are established. Extensive simulation studies are conducted to examine the performance of our proposed procedures in finite samples. Finally, these methods are applied to the well-known chronic granulomatous disease study.
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- Li, Jianbo & Gu, Minggao, 2012. "Adaptive LASSO for general transformation models with right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2583-2597.
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