Supplementary Materials for A Diagnostic of Influential Cases Based on the Information Complexity Criteria in Generalized Linear Mixed Models
نویسنده
چکیده
Abstract: Modeling diagnostics assess models by means of a variety of criteria. Each criterion typically performs its evaluation upon a specific inferential objective. For instance, the well-known DFBETAS in linear regression models are a modeling diagnostic which is applied to discover the influential cases in fitting a model. To facilitate the evaluation of generalized linear mixed models (GLMM), we develop a diagnostic for detecting influential cases based on the Information Complexity Criteria (ICOMP) for detecting the influential cases which substantially affect the model selection criterion ICOMP. In a given model, the diagnostic compares the ICOMP between the full data set and a case-deleted data set. The computational formula of the ICOMP is evaluated using the Fisher information matrix. A simulation study is accomplished and a real data set of cancer cells is analyzed using the logistic linear mixed model for illustrating the effectiveness of the proposed diagnostic in detecting the influential cases.
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تاریخ انتشار 2013