© 2004 by Biometrika Trust
Temporal process regression
1 Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, U.S.A. fine{at}stat.wisc.edu jyan{at}stat.wisc.edu kosorok{at}stat.wisc.edu
We consider regression for response and covariates which are temporal processes observed over intervals. A functional generalised linear model is proposed which includes extensions of standard models in multi-state survival analysis. Simple nonparametric estimators of time-indexed parameters are developed using ‘working independence’ estimating equations and are shown to be uniformly consistent and to converge weakly to Gaussian processes. The procedure does not require smoothing or a Markov assumption, unlike approaches based on transition intensities. The usual definition of optimal estimating equations for parametric models is then generalised to the functional model and the optimum is identified in a class of functional generalised estimating equations. Simulations demonstrate large efficiency gains relative to working independence at times where censoring is heavy. The estimators are the basis for new tests of the covariate effects and for the estimation of models in which greater structure is imposed on the parameters, providing novel goodness-of-fit tests. The methodology's practical utility is illustrated in a data analysis.
Key Words: Empirical process; Event history; Godambe optimality; Hilbert space; Non-Markov; Partially observed; Score operator; Uniform convergence; Varying coefficient
Received January 2003. Revised January 2004.
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