Semiparametric partially linear varying coefficient models with panel count data |
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Authors: | Xin He Xuenan Feng Xingwei Tong Xingqiu Zhao |
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Institution: | 1.University of Maryland,College Park,USA;2.The Hong Kong Polytechnic University,Hung Hom,Hong Kong;3.Beijing Normal University,Beijing,China |
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Abstract: | This paper studies semiparametric regression analysis of panel count data, which arise naturally when recurrent events are considered. Such data frequently occur in medical follow-up studies and reliability experiments, for example. To explore the nonlinear interactions between covariates, we propose a class of partially linear models with possibly varying coefficients for the mean function of the counting processes with panel count data. The functional coefficients are estimated by B-spline function approximations. The estimation procedures are based on maximum pseudo-likelihood and likelihood approaches and they are easy to implement. The asymptotic properties of the resulting estimators are established, and their finite-sample performance is assessed by Monte Carlo simulation studies. We also demonstrate the value of the proposed method by the analysis of a cancer data set, where the new modeling approach provides more comprehensive information than the usual proportional mean model. |
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