Partly functional temporal process regression with semiparametric profile estimating functions

Jun Yan; Jian Huang

doi:10.1111/j.1541-0420.2008.01071.x

Back

Journal article

Peer reviewed

Partly functional temporal process regression with semiparametric profile estimating functions

Jun Yan and Jian Huang

Biometrics, Vol.65(2), pp.431-440

06/2009

DOI: 10.1111/j.1541-0420.2008.01071.x

PMID: 18547299

View Online

Abstract

Marginal mean models of temporal processes in event time data analysis are gaining more attention for their milder assumptions than the traditional intensity models. Recent work on fully functional temporal process regression (TPR) offers great flexibility by allowing all the regression coefficients to be nonparametrically time varying. The existing estimation procedure, however, prevents successive goodness-of-fit test for covariate coefficients in comparing a sequence of nested models. This article proposes a partly functional TPR model in the line of marginal mean models. Some covariate effects are time independent while others are completely unspecified in time. This class of models is very rich, including the fully functional model and the semiparametric model as special cases. To estimate the parameters, we propose semiparametric profile estimating equations, which are solved via an iterative algorithm, starting at a consistent estimate from a fully functional model in the existing work. No smoothing is needed, in contrast to other varying-coefficient methods. The weak convergence of the resultant estimators are developed using the empirical process theory. Successive tests of time-varying effects and backward model selection procedure can then be carried out. The practical usefulness of the methodology is demonstrated through a simulation study and a real example of recurrent exacerbation among cystic fibrosis patients.

Algorithms

Epidemiologic Research Design

Regression Analysis

Risk Assessment - methods

Data Interpretation, Statistical

Reproducibility of Results

Computer Simulation

Sensitivity and Specificity

Proportional Hazards Models

Pattern Recognition, Automated

Biometry - methods

Cluster Analysis

Details

Title: Subtitle: Partly functional temporal process regression with semiparametric profile estimating functions
Creators: Jun Yan - Department of Statistics, University of Connecticut, Unit 4120, Storrs, Connecticut 06269, USA. jun.yan@uconn.edu
Jian Huang
Resource Type: Journal article
Publication Details: Biometrics, Vol.65(2), pp.431-440
DOI: 10.1111/j.1541-0420.2008.01071.x
PMID: 18547299
NLM abbreviation: Biometrics
ISSN: 1541-0420
eISSN: 1541-0420
Publisher: United States
Language: English
Date published: 06/2009
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9983985917202771

Metrics

26 Record Views

4 Times Cited - Web of Science