Robust Estimation of Semiparametric Transformation Model for Panel Count Data

Panel count data are frequently encountered when study subjects are under discrete observations. However, limited literature has been found on variable selection for panel count data. In this paper, without considering the model assumption of observation process, a more general semiparametric transformation model for panel count data with informative observation process is developed. A penalized estimation procedure based on the quantile regression function is proposed for variable selection and parameter estimation simultaneously. The consistency and oracle properties of the estimators are established under some mild conditions. Some simulations and an application are reported to evaluate the proposed approach.