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



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



Journal Of Computational And Graphical Statistics: 4, 870-891



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.

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Accession: 033220280

Download citation: RISBibTeXText

DOI: 10.1198/106186008x386599


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