This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 499
Type: Topic Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #306746
Title: Functional Mixed Effects Modeling by Parameter Cascading
Author(s): Jiguo Cao*+ and James Owen Ramsay
Companies: Simon Fraser University and McGill University
Address: Department of Statistics and Actuarial Science, Burnaby, BC, V5A1S6 , Canada
Keywords: marginalization ; regularization ; nuisance parameters ; mixed effect smoothing
Abstract:

A linear mixed effects model (LME) is a familiar example of a multi-level parameter structure involving nuisance and structural parameters, as well as parameters that essentially control the model's complexity. Marginalization over nuisance parameters, such as the restricted maximization likelihood method, has been the usual estimation strategy; but can involve onerous and complex algorithms to achieve the integrations involved. Parameter cascading is described as a multi-criterion optimization algorithm that is relatively simple to program and leads to fast and stable computation. The method is applied to functional mixed effects modeling by introducing a basis partitioning scheme and defining roughness penalty terms for both functional fixed effect and random effects. The results are substantially better than those obtained by using the previous LME methods.


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