Robust Estimation of Semiparametric Transformation Model for Panel Count Data
被引:2
作者:
Feng Yan
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Shanxi Med Univ, Hosp 1, Dept Otolaryngol Head & Neck Surg, Taiyuan 030001, Peoples R ChinaShanxi Med Univ, Hosp 1, Dept Otolaryngol Head & Neck Surg, Taiyuan 030001, Peoples R China
Feng Yan
[1
]
Wang Yijun
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机构:
Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou 310018, Peoples R ChinaShanxi Med Univ, Hosp 1, Dept Otolaryngol Head & Neck Surg, Taiyuan 030001, Peoples R China
Wang Yijun
[2
]
Wang Weiwei
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Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou 310018, Peoples R ChinaShanxi Med Univ, Hosp 1, Dept Otolaryngol Head & Neck Surg, Taiyuan 030001, Peoples R China
Wang Weiwei
[2
]
Chen Zhuo
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Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210014, Peoples R ChinaShanxi Med Univ, Hosp 1, Dept Otolaryngol Head & Neck Surg, Taiyuan 030001, Peoples R China
Chen Zhuo
[3
]
机构:
[1] Shanxi Med Univ, Hosp 1, Dept Otolaryngol Head & Neck Surg, Taiyuan 030001, Peoples R China
[2] Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou 310018, Peoples R China
[3] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210014, Peoples R China
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.