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General Departmental Seminar Series


Functional Regression Models and Temporal Processes

Jun Yan, Ph.D. Candidate, Department of Statistics, UW-Madison

Friday, December 6, 2002, 12:00 pm

G5/136-142 Clinical Sciences Center, 600 Highland Ave.

ABSTRACT

We consider regression for response and covariates which are temporal processes observed over intervals. A functional generalized linear model is proposed which includes extensions of standard models in multistate survival analysis. Simple nonparametric estimators of time-indexed parameters are presented and 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 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. The methodology enables goodness-of-fit testing and permits predictions involving estimated components from both the functional model and the submodels. Its practical utility is illustrated in recurrent event simulations and a data analysis of the prevalence of Chronic Graft Versus Host Disease (CGVHD).


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