Linear Mixed-Effects Modeling by Parameter Cascading
成果类型：
Article
署名作者：
Cao, J.; Ramsay, J. O.
署名单位：
Simon Fraser University; McGill University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/jasa.2009.tm09124
发表日期：
2010
页码：
365-374
关键词：
摘要：
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
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