Self-normalized, score-based tests of mixed models
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Score-based tests have been used to study parameter heterogeneity across many types of statistical models. This chapter describes a new self-normalization approach for score-based tests of mixed models, which addresses situations where there is dependence between scores. This differs from the traditional score-based tests, which require independence of scores. We first review traditional score-based tests and then propose a new, self-normalized statistic that is related to previous work by Shao and Zhang (2010) and Zhang, Shao, Hayhoe, and Wuebbles (2011). We then provide simulation studies that demonstrate how traditional score-based tests can fail when scores are dependent, and that also demonstrate the good performance of the self-normalized tests. Next, we illustrate how the statistics can be used with real data. Finally, we discuss the potential broad application of self-normalized, score-based tests in mixed models and other models with dependent observations.
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