Semiparametric partially linear varying coefficient models with panel count data

Xin He, Xuenan Feng, Xingwei Tong and Xingqiu Zhao ()
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Xin He: University of Maryland
Xuenan Feng: The Hong Kong Polytechnic University
Xingwei Tong: Beijing Normal University
Xingqiu Zhao: The Hong Kong Polytechnic University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2017, vol. 23, issue 3, No 6, 439-466

Abstract: 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.

Keywords: Asymptotic normality; B-spline; Counting process; Maximum likelihood; Maximum pseudo-likelihood; Panel count data; Varying-coefficient (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10985-016-9368-x

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