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  4. modelSampler: An R Tool for Variable Sel ...

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modelSampler: An R Tool for Variable Selection and Model Exploration in Linear Regression
Volume 11, Issue 2 (2013), pp. 343–370
Tanujit Dey  

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https://doi.org/10.6339/JDS.2013.11(2).1133
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

Abstract: We have developed a tool for model space exploration and variable selec tion in linear regression models based on a simple spike and slab model (Dey, 2012). The model chosen is the best model with minimum final prediction error (FPE) values among all other models. This is implemented via the R package modelSampler. However, model selection based on FPE criteria is dubious and question able as FPE criteria can be sensitive to perturbations in the data. This R package can be used for empirical assessment of the stability of FPE criteria. A stable model selection is accomplished by using a bootstrap wrapper that calls the primary function of the package several times on the bootstrapped data. The heart of the method is the notion of model averaging for sta ble variable selection and to study the behavior of variables over the entire model space, a concept invaluable in high dimensional situations.

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Keywords
FPE analysis model exploration model uncertainty

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