A diagnostic of influential cases based on the information complexity criteria in generalized linear mixed models

Junfeng Shang

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 13, 3751-3760

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 (ICOMP) criteria for detecting influential cases which substantially affect the model selection criterion ICOMP. In a given model, the diagnostic compares the ICOMP criterion between the full data set and a case-deleted data set. The computational formula of the ICOMP criterion 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.

Date: 2016
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DOI: 10.1080/03610926.2014.911902

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