Categorical variables with many categories are preferentially selected in bootstrap-based model selection procedures for multivariable regression models

DOI10.1002/bimj.201400185zbMath1386.62063OpenAlexW2310986303WikidataQ39898425 ScholiaQ39898425MaRDI QIDQ2806852

Silke Janitza, Susanne Rospleszcz, Anne-Laure Boulesteix

Publication date: 19 May 2016

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201400185

zbMATH Keywords

likelihood ratio testmodel selectioncategorical variablesbootstrap samplesautomated selection procedures

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (2)

Detection of influential points as a byproduct of resampling-based variable selection procedures ⋮ Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications

Uses Software

• R
• GenOrd
• catdata

Cites Work

• Unnamed Item
• Subsampling versus bootstrapping in resampling-based model selection for multivariable regression
• Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications
• Multivariable Model‐Building
• Bootstrap Methods for Developing Predictive Models
• The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses

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