Please use this identifier to cite or link to this item:
Title: Semiparametric partially linear varying coefficient models with panel count data
Authors: He, X
Feng, XN 
Tong, XW
Zhao, XQ 
Keywords: Asymptotic normality
Counting process
Maximum likelihood
Maximum pseudo-likelihood
Panel count data
Issue Date: 2017
Publisher: Springer
Source: Lifetime data analysis, 2017, v. 23, no. 3, p. 439-466 How to cite?
Journal: Lifetime data analysis 
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.
ISSN: 1380-7870
EISSN: 1572-9249
DOI: 10.1007/s10985-016-9368-x
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.