{"data":{"id":"10.6084/m9.figshare.c.2119007","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2119007","prefix":"10.6084","suffix":"m9.figshare.c.2119007","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Junfeng Shang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"A diagnostic of influential cases based on the information complexity criteria in generalized linear mixed models"}],"publisher":"Figshare","container":{},"publicationYear":2015,"subjects":[{"subject":"Space Science"},{"subject":"Medicine"},{"subject":"Biotechnology"},{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Cancer"},{"subject":"Inorganic Chemistry"},{"subject":"FOS: Chemical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Chemical sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2016-05-17","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"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.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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