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Candidate Model Choice in Feature-Based Model Combination

19 Pages Posted: 17 Jan 2011  

Mingyang Xu

Massachusetts Institute of Technology (MIT)

Michael Golay

affiliation not provided to SSRN

Date Written: 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.

Keywords: Candidate model choice, feature-based model combination, model assessment, Bayesian information aggregation

JEL Classification: C13, C14, C44, C51, C52, C61

Suggested Citation

Xu, Mingyang and Golay, Michael, Candidate Model Choice in Feature-Based Model Combination (March 1, 2007). Available at SSRN: or

Mingyang Xu (Contact Author)

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Michael Golay

affiliation not provided to SSRN ( email )

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