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Restricted likelihood ratio testing for zero variance components in linear mixed models.
J. Comput. Graph. Stat. 17, 870-891 (2008)
The goal of our article is to provide a transparent, robust, and computationally feasible statistical platform for restricted likelihood ratio testing (RLRT) for zero variance components in linear mixed models. This problem is nonstandard because under the null hypothesis the parameter is on the boundary of the parameter space. Our proposed approach is different from the asymptotic results of Stram and Lee who assumed that the outcome vector can be partitioned into many independent subvectors. Thus, our methodology applies to a wider class of mixed models, which includes models with a moderate number Of Clusters or nonparametric smoothing components. We propose two approximations to the finite sample null distribution of the RLRT statistic. Both approximations converge weakly to the asymptotic distribution obtained by Strain and Lee when their assumptions hold. When their assumptions do not hold, we show in extensive simulation studies that both approximations outpertform the Strain and Lee approximation and the parametric bootstrap. We also identify and address numerical problems associated with standard mixed model software. Our methods are motivated by and applied to a large longitudinal study on air Pollution health effects in a highly Susceptible cohort. Relevant software is posted as an online supplement.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Parametric Bootstrap; Nonparametric smoothing; Nonregular problem; Penalized splines; particulate air-pollution; penalized splines; regression
ISSN (print) / ISBN
1061-8600
e-ISSN
1537-2715
Quellenangaben
Volume: 17,
Issue: 4,
Pages: 870-891
Publisher
Taylor & Francis
Publishing Place
Alexandria
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Epidemiology (EPI)