The probabilistic reduction approach to specifying multinomial logistic regression models in health outcomes research

DOI10.1080/02664763.2014.909785zbMath1352.62158OpenAlexW2123049041MaRDI QIDQ2953286

Eberechukwu Onukwugha, Jason S. Bergtold

Publication date: 4 January 2017

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2014.909785

zbMATH Keywords

heterogeneitymultinomial logistic regressionmodel specificationinteraction effectsmarginal effectsprobabilistic reduction approach

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Comparison of penalized logistic regression models for rare event case

Cites Work

• Akaike-type criteria and the reliability of inference: model selection versus statistical model specification
• Key-minimal cryptosystems for unconditional secrecy
• Conditional specification of statistical models.
• Transformations of the explanatory variables in the logistic regression model for binary data
• Discrete Choice Methods with Simulation
• A Simulation Study to Investigate the Behavior of the Log-Density Ratio Under Normality
• Model Selection and Multimodel Inference
• Separate sample logistic discrimination

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