Determination of the best significance level in forward stepwise logistic regression

DOI10.1080/03610919708813397zbMath0900.62399OpenAlexW2140273368WikidataQ126236180 ScholiaQ126236180MaRDI QIDQ4369362

John J. Koval, Kang-In David Lee

Publication date: 18 December 1997

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610919708813397

zbMATH Keywords

predictionMonte Carlo simulationmultivariate normal distributionmultivariate binary distribution

Mathematics Subject Classification ID

Multivariate analysis (62H99) Point estimation (62F10) Linear inference, regression (62J99)

Related Items (4)

Variable Selection in Logistic Regression Models ⋮ A modified multinomial baseline logit model with logit functions having different covariates ⋮ On variable selection in joint modeling of mean and dispersion ⋮ Determination of the Selection Statistics and Best Significance Level in Backward Stepwise Logistic Regression

Uses Software

• SAS

Cites Work

• Unnamed Item
• Estimating the dimension of a model
• Best Subsets Logistic Regression
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• How Biased is the Apparent Error Rate of a Prediction Rule?
• Comparison of Stopping Rules in Forward Stepwise Discriminant Analysis
• A Stepwise Variable Selection Procedure for Nonlinear Regression Models
• Comparison of Stopping Rules in Forward "Stepwise" Regression
• Sample-Based Classification Procedures Derived from Density Estimators

This page was built for publication: Determination of the best significance level in forward stepwise logistic regression