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

作者：Shang Junfeng^*

来源：Communications in Statistics - Theory and Methods, 2016, 45(13): 3751-3760.

DOI：10.1080/03610926.2014.911902

• Case-deletion
• Fisher information matrix
• GLMM
• ICOMP
• logistic regression model

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.

• 出版日期2016