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.

当研究对象处于离散观察下时，经常会遇到面板计数数据。然而，关于面板计数数据的变量选择的文献有限。本文在不考虑观测过程的模型假设的前提下，开发了一种更通用的具有信息观测过程的面板计数数据半参数转换模型。提出了一种基于分位数回归函数的惩罚估计程序，用于变量选择和参数估计同时进行。在某些温和条件下确定了估计量的一致性和预言性。报告了一些仿真和应用程序，以评估所提出的方法。