JSM 2005 - Toronto

Abstract #302747

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 436
Type: Invited
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #302747
Title: Functional Mixed Effects Models with Prior Information
Author(s): Wensheng Guo*+ and Li Qin
Companies: University of Pennsylvania and Fred Hutchinson Cancer Research Center
Address: CCEB Division of Biostatistics, Philadelphia, PA, 19104,
Keywords:
Abstract:

In functional data analysis, the basic unit of the data analysis is a curve, so are the parameters. In many situations, some prior information about the curves is available, such as the curves are similar in shape to a family of parametric functions or they are subjected to some linear equality constraints. Such information needs to be accounted for in the model to improve the estimation and inference. In this paper, we propose a new class of functional mixed effects models that incorporate the prior information in both the functional fixed effects and functional random effects. The functional fixed effects and functional random effects are modeled in the same functional space, and therefore the population-average curves and subject-specific curves have the same property. We develop a straightforward construction of state space models for L-splines. The whole proposed functional mixed effects models can be rewritten in multivariate state space forms and estimated by an O(N) modified Kalman filtering and smoothing algorithm. These models are applied to a cortisol dataset obtained from a study on fibromyalgia.

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Revised March 2005