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Abstract: The small-sample performance of alternatives to the usual likelihood ratio test in mixed linear models is investigated. Specifically, the following tests for fixed effects are considered: (i) a bootstrap-based test, (ii) the Bartlett-corrected usual test, and (iii) an adjusted profile likelihood ratio test. The last test is derived using an approximation to the modified profile likelihood proposed by Barndorff-Nielsen, based on the work of Severini. Bootstrap resampling is performed to numerically construct a Bartlett correction factor for the usual test statistic, and also to obtain a critical value that does not rely on first-order asymptotics. The numerical evidence presented in the paper slightly favors the Bartlett-corrected usual test. An application to real longitudinal data is presented. [Copyright &y& Elsevier]

Abstract: The joint segmentation of multiple series is considered. A mixed linear model is used to account for both covariates and correlations between signals. An estimation algorithm based on EM which involves a new dynamic programming strategy for the segmentation step is proposed. The computational efficiency of this procedure is shown and its performance is assessed through simulation experiments. Applications are presented in the field of climatic data analysis. [ABSTRACT FROM AUTHOR]

The small sample performance of several procedures for testing a given fixed effect in a mixed linear model is investigated. Using simulations, constructed on the basis of a study of growth of children with Gaucher''s disease, standard normal-theory Wald tests for both ML and REML estimates, the likelihood ratio test (LRT), a modified LRT based on Bartlett correction, and a number of adjusted tests based on t and F distributions are evaluated. Methods used for determining the denominator degrees of freedom in the t and F tests include the residual degrees of freedom method, the between and within degrees of freedom, the containment method, the naive method and the Satterthwaite method. A test based on a sandwich-type estimator of the variance of the parameter estimate is evaluated as well and the effect of mis-specifying the random-effects distribution is considered. Results show that Type I error rates for the Wald-type test with chi-square approximation are substantially inflated, though less so with REML estimates than with ML estimates. The LRT based on ML estimates yielded Type I error rates similar to those observed for the Wald-type chi-square test with REML estimates. A substantial improvement in Type I error rates for testing on both the intercept and slope is provided by each of the three following modifications: the Satterthwaite and naive methods with REML-based estimates and the Bartlett-corrected LRT. [Copyright &y& Elsevier]