Inferences from logistic regression models in the presence of small samples, rare events, nonlinearity, and multicollinearity with observational data

DOI10.1080/02664763.2017.1282441OpenAlexW2584211670MaRDI QIDQ5139010

Elizabeth A. Yeager, Allen M. Featherstone, Jason S. Bergtold

Publication date: 4 December 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2017.1282441

zbMATH Keywords

rare eventsnonlinearityinferencemulticollinearitylogistic regression modelsmall sample bias

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Comparison of penalized logistic regression models for rare event case

Uses Software

• Matlab
• aplore3

Cites Work

• Approximate bias correction in econometrics
• A quadratic bootstrap method and improved estimation in logistic regression.
• Conditional specification of statistical models.
• The effects of sampling strategies on the small sample properties of the logit estimator
• Transformations of the explanatory variables in the logistic regression model for binary data
• Regression analysis and problems of multicollinearity
• A Simulation Study to Investigate the Behavior of the Log-Density Ratio Under Normality
• Small-sample bias and corrections for conditional maximum-likelihood odds-ratio estimators
• Bias reduction of maximum likelihood estimates
• Applied Logistic Regression
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