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Greven, S.* ; Crainiceanu, C.M.* ; Küchenhoff, H.* ; Peters, A.

Restricted likelihood ratio testing for zero variance components in linear mixed models.

J. Comput. Graph. Stat. 17, 870-891 (2008)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Parametric Bootstrap; Nonparametric smoothing; Nonregular problem; Penalized splines; particulate air-pollution; penalized splines; regression
ISSN (print) / ISBN 1061-8600
e-ISSN 1537-2715
Zeitschrift Journal of Computational and Graphical Statistics
Quellenangaben Band: 17, Heft: 4, Seiten: 870-891 Artikelnummer: , Supplement: ,
Verlag Taylor & Francis
Verlagsort Alexandria
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)