Comparing Stochastic Optimization Methods for Variable Selection in Binary Outcome Prediction, With Application to Health Policy

DOI10.1198/016214508000001048zbMath1286.62065OpenAlexW2158274052MaRDI QIDQ5414012

Dimitris Fouskakis, David Draper

Publication date: 2 May 2014

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/016214508000001048

zbMATH Keywords

predictionsimulated annealingtabu searchMonte Carlo methodscross-validationlogistic regressiongenetic algorithmvariable selectionBayesian decision theoryinput-output analysisquality of health caremaximization of expected utilitysickness at hospital admission

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian problems; characterization of Bayes procedures (62C10) Generalized linear models (logistic models) (62J12)

Related Items (6)

A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis ⋮ Integrating linear discriminant analysis, polynomial basis expansion, and genetic search for two-group classification ⋮ Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods ⋮ Economic variable selection ⋮ A survey of Bayesian predictive methods for model assessment, selection and comparison ⋮ Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care

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