Robust variable selection of joint frailty model for panel count data

Panel count data are generated from studies that concern recurrent events or event history studies in which the subjects are observed only at specific points in time. Recently, research on panel count data has drawn considerable attention. The literature on variable selection of panel count data has so far been quite limited. In this paper, a robust variable selection approach based on the quantile regression function in a joint frailty model is proposed to analyze panel count data. A three-step estimation method is introduced to estimate the coefficients and unknown functions. Consistency and oracle properties are established under some mild regularity conditions. Simulations are used to assess the proposed estimation method. Bladder tumor cancer data are also re-analyzed as an illustration.