Standard asymptotic chi-square distribution of the likelihood ratio and score statis- tics under the null hypothesis does not hold when the parameter value is on the boundary of the parameter space. In mixed models it is of interest to test for a zero random effect variance component. Some available tests for the variance compo- nent are reviewed and a new test within the permutation framework is presented. The power and significance level of the different tests are investigated by means of a Monte Carlo simulation study. The level of the proposed test is closer to the nominal significance level and is more powerful.
The use of permutation tests for variance components in linear mixed models
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Abstract
Standard asymptotic chi-square distribution of the likelihood ratio and score statis- tics under the null hypothesis does not hold when the parameter value is on the boundary of the parameter space. In mixed models it is of interest to test for a zero random effect variance component. Some available tests for the variance compo- nent are reviewed and a new test within the permutation framework is presented. The power and significance level of the different tests are investigated by means of a Monte Carlo simulation study. The level of the proposed test is closer to the nominal significance level and is more powerful.File in questo prodotto:
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