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Activity Number: 274 - SPES and Q&P Student Paper Award
Type: Topic-Contributed
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #317257
Title: Multi-Model Penalized Regression
Author(s): Laura Wendelberger* and Brian Reich and Alyson Wilson
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Keywords: model uncertainty; variable selection; model averaging; penalized regression

Model fitting often aims to fit a single model, assuming that the imposed form of the model is correct. However, there may be multiple possible underlying explanatory patterns in a set of predictors that could explain a response. Model selection without regarding model uncertainty can fail to bring these patterns to light. We present multi-model penalized regression (MMPR) to acknowledge model uncertainty in the context of penalized regression. In the penalty form explored here, we examine how different settings can promote either shrinkage or sparsity of coefficients in separate models. The method is tuned to explicitly limit model similarity. A choice of penalty form that enforces variable selection is applied to predict stacking fault energy (SFE) from steel alloy composition. The aim is to identify multiple models with different subsets of covariates that explain a single type of response.

Authors who are presenting talks have a * after their name.

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