Abstract
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.
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Acknowledgements
The authors would like to thank the Editor, an associate editor, and two anonymous reviewers for their constructive comments that have significantly improved the paper. We thank Professor Zhao Xiaobing and Zhou Xian to help improve the English presentation.
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This paper was partially supported by the National Natural Science Foundation of China under Grant No. 12001485, the National Bureau of Statistics of China under Grant No. 2020LY073, and the First Class Discipline of Zhejiang-A (Zhejiang University of Finance and Economics- Statistics) under Grant No. Z0111119010/024.
This paper was recommended for publication by Editor ZHU Liping.
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Feng, Y., Wang, Y., Wang, W. et al. Robust Estimation of Semiparametric Transformation Model for Panel Count Data. J Syst Sci Complex 34, 2334–2356 (2021). https://doi.org/10.1007/s11424-020-0099-4
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DOI: https://doi.org/10.1007/s11424-020-0099-4