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Abstract Details

Activity Number: 190
Type: Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract - #304709
Title: Model Evaluation, Discrepancy Function Estimation, and Social Choice Theory
Author(s): Andrew Neath*+ and Joseph Cavanaugh and Adam Weyhaupt
Companies: SIU Edwardsville and University of Iowa and SIU Edwardsville
Address: Dept of Math and Stats, Edwardsville, IL, 62026, United States
Keywords: model selection ; resampling ; conceptual predictive statistic

A discrepancy function provides for an evaluation of a candidate model by quantifying the disparity between the candidate model and the true model which generated the observed data. The best model from a candidate class is the one judged to have minimum discrepancy with the true model. Since data provides only partial information on the underlying true model, we consider taking repeated simulations using some resampling procedure as each representing some possibility for the true model. Each of the candidate models can be judged against each of the simulated versions of the true model. Model evaluation is then achieved by providing an overall judgment on each candidate model. As social choice theory, or voting theory, addresses the problem of turning individual preferences into a group preference, we see that social choice theory can be used in developing a new approach to model evaluation.

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