Candidate Model Choice in Feature-Based Model Combination
Massachusetts Institute of Technology (MIT)
affiliation not provided to SSRN
March 1, 2007
This paper is intended to solve a central problem in recently developed feature-based model combination method (Xu and Golay, 2005), that is, how to choose candidate models. Through our analysis, we first conclude that the efficiency of model combination highly depends on the choice of candidate models. Some desirable properties are then proposed to assess a group of candidate models, which include accuracy, diversity, independence as well as completeness. To facilitate the choice with the use of these criteria, some quantitative measures are put forward. Meanwhile, Bayesian method and utility function are employed to aggregate information to obtain an overall evaluation of models. Finally, a stepwise forward candidate model choice procedure is proposed to realize all these criteria in a procedure, which chooses a group of candidate models out of a model pool.
Number of Pages in PDF File: 19
Keywords: Candidate model choice, feature-based model combination, model assessment, Bayesian information aggregation
JEL Classification: C13, C14, C44, C51, C52, C61
Date posted: January 17, 2011
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