Data · collection · 2015
A diagnostic of influential cases based on the information complexity criteria in generalized linear mixed models
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Modeling diagnostics assess models by means of a variety of criteria.
Description
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.
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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.
Links
Where it is published
- Repository landing page figshare.com/collections/A_Diagnostic_of_Influential_Cases_Based_on_the_Inf… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.c.2119007 ↗
DOI / persistent id · from DataCite
Documentation and papers
- CC-BY creativecommons.org/licenses/by/3.0/us ↗
license · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.c.2119007 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.c.2119007 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Biological sciences · Chemical sciences · Computer and information sciences · Mathematics
- From keywords
- Cancer · Medicine & Health
- Inferred from text
- Simulation 75%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataCite | 10.6084/m9.figshare.c.2119007 | 15 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · DataCite | connector:datacite@1.0.0 | /data/attributes/rightsList |
| concepts[disease].local:disease:cancer | mapping · DataCite | vocabulary-mapper@1.0.0 | keywords['Cancer'] |
| concepts[field].fos:biological-sciences | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:chemical-sciences | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:computer-and-information-sciences | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:mathematics | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · DataCite | vocabulary-mapper@1.0.0 | keywords['Medicine'] |
| concepts[method].local:method:simulation | enrichment · DataCite | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · DataCite | connector:datacite@1.0.0 | |
| description | source · DataCite | connector:datacite@1.0.0 | /data/attributes/descriptions |
| license | source · DataCite | connector:datacite@1.0.0 | /data/attributes/rightsList |
| publication_date | source · DataCite | connector:datacite@1.0.0 | /data/attributes/dates |
| title | source · DataCite | connector:datacite@1.0.0 | /data/attributes/titles/0/title |
| updated_date | source · DataCite | connector:datacite@1.0.0 |