Increasing generalizability via the principle of minimum description length
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Published on: June 25, 2019
1Missouri Prevention Science Institute, University of Missouri, Columbia, MO65211, USAbonifayw@missouri.eduhttps://education.missouri.edu/person/wes-bonifay/.
Abstract:
Traditional statistical model evaluation typically relies on goodness-of-fit testing and quantifying model complexity by counting parameters. Both of these practices may result in overfitting and have thereby contributed to the generalizability crisis. The information-theoretic principle of minimum description length addresses both of these concerns by filtering noise from the observed data and consequently increasing generalizability to unseen data.
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