Linear Mixed-Effects Modeling by Parameter Cascading

Jiguo Cao

doi:10.1198/jasa.2009.tm09124

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Linear Mixed-Effects Modeling by Parameter Cascading

2012-01-01

PRE

Jiguo Cao *

J. O. Ramsay

DOI：10.1198/jasa.2009.tm09124

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Abstract

En 中文

A linear mixed-effects model (LME) is a familial example of a multilevel 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 it can Involve onerous and complex algorithms to achieve the integrations involved Parameter cascading Is described as a multicriterion optimization algorithm that is relatively simple to program and leads to fast and stable computation The method is applied 10 LME. where well-developed marginalization methods are already available Our results suggest that parameter cascading is at least as good as. if not better than. the available methods We also extend the LME model to multicurve data smoothing by introducing. a basis partitioning scheme and defining. toughness penalty terms for both functional fixed effect and random effects The results are substantially better than those obtained by using the previous LME methods A supplemental document Is available online

Keywords:

Marginalization

Mixed-Effect smoothing

Model complexity

Nuisance parameters

Regularization

Variance components

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