American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



BAYESIAN WAVELET-BASED MIXED MODELS FOR FUNCTIONAL DATA

by

Jeff Morris
MD Anderson Cancer Center


Abstract

Many studies yield functional data, with the ideal units of observation curves and observed data sampled on a fine grid. These curves frequently have irregular features requiring spatially adaptive nonparametric representations. We discuss new methods for modeling these data using functional mixed models, which treat the curves as responses and relate them to covariates using nonparametric fixed and random effect functions. This Bayesian wavelet-based approach yields adaptively regularized posterior samples for all model parameters that can be used for any desired Bayesian estimation, inference or prediction. We illustrate this method on four applications yielding spiky functional data, and describe how it can be extended to deal with incomplete functional data for which some regions of some of the functions are missing, and to model higher dimensional functional data, e.g. images.


Date: Thursday, February 15, 2007
Time: 4:00 to 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Judith Jansen Conference Room (Room 425)
New York, New York

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (Room 627).


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