 Semiparametric regression analysis of panel count data with informative observation timesXingqiu Zhao and Xingwei Tong Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 291-300 Abstract: This paper discusses regression analysis of panel count data that arise naturally when recurrent events are considered. For the analysis of panel count data, most of the existing methods have assumed that observation times are completely independent of recurrent events or given covariates, which may not be true in practice. We propose a joint modeling approach that uses an unobserved random variable and a completely unspecified link function to characterize the correlations between the response variable and the observation times. For inference about regression parameters, estimating equation approaches are developed without involving any estimation for latent variables, and the asymptotic properties of the resulting estimators are established. In addition, a technique is provided for assessing the adequacy of the model. The performance of the proposed estimation procedures are evaluated by means of Monte Carlo simulations, and a data set from a bladder tumor study is analyzed as an illustrative example. Keywords: Estimating; equation; Informative; observation; times; Mean; function; model; Panel; count; data; Regression; analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:291-300 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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