The Use of Resampling Methods to Simplify Regression Models in Medical Statistics
From MaRDI portal
Publication:4262923
DOI10.1111/1467-9876.00155zbMath0939.62114OpenAlexW2073790637MaRDI QIDQ4262923
Publication date: 3 July 2000
Published in: Journal of the Royal Statistical Society Series C: Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/1467-9876.00155
predictionbootstrapcross-validationselection biasmodel complexitybackward eliminationselection level
Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric statistical resampling methods (62G09)
Related Items (9)
A new variable selection approach using random forests ⋮ On stability issues in deriving multivariable regression models ⋮ The practical utility of incorporating model selection uncertainty into prognostic models for survival data ⋮ Variable selection – A review and recommendations for the practicing statistician ⋮ Model selection in linear regression using paired bootstrap ⋮ An overview of techniques for linking high‐dimensional molecular data to time‐to‐event endpoints by risk prediction models ⋮ Multivariable regression model building by using fractional polynomials: description of SAS, STATA and R programs ⋮ Subsampling versus bootstrapping in resampling-based model selection for multivariable regression ⋮ Handling co-dependence issues in resampling-based variable selection procedures: a simulation study
This page was built for publication: The Use of Resampling Methods to Simplify Regression Models in Medical Statistics