Please use this identifier to cite or link to this item: https://doi.org/10.1198/jasa.2011.tm10592
Title: A semiparametric threshold model for censored longitudinal data analysis
Authors: Li, J. 
Zhang, W.
Keywords: ABIC
Censored longitudinal data
Kernel smoothing
Local maximum likelihood estimation
Quadratic approximation
Semiparametric model
Issue Date: Jun-2011
Citation: Li, J., Zhang, W. (2011-06). A semiparametric threshold model for censored longitudinal data analysis. Journal of the American Statistical Association 106 (494) : 685-696. ScholarBank@NUS Repository. https://doi.org/10.1198/jasa.2011.tm10592
Abstract: Motivated by an investigation of the relationship between blood pressure change and progression of microalbuminuria (MA) among individuals with type I diabetes, we propose a new semiparametric threshold model for censored longitudinal data analysis.We also study a new semiparametric Bayes information criterion-type criterion for identifying the parametric component of the proposed model. Cluster effects in the model are implemented as unknown fixed effects. Asymptotic properties are established for the proposed estimators. A quadratic approximation used to implement the estimation procedure makes the method very easy to implement by avoiding the computation of multiple integrals and the need for iterative algorithms. Simulation studies show that the proposed methods work well in practice. An illustration using the Wisconsin Diabetes Registry dataset suggests some interesting findings. © 2011 American Statistical Association.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/104968
ISSN: 01621459
DOI: 10.1198/jasa.2011.tm10592
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