Testing for more positive expectation dependence with application to model comparison

Michel Denuit (), Julien Trufin () and Thomas Verdebout
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Michel Denuit: Université catholique de Louvain, LIDAM/ISBA, Belgium
Julien Trufin: ULB
Thomas Verdebout: ULB

No 2021021, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: Modern data science tools are effective to produce predictions that strongly correlate with responses. Model comparison can therefore be based on the strength of dependence between responses and their predictions. Positive expectation dependence turns out to be attractive in that respect. The present paper proposes an effective testing procedure for this dependence concept and applies it to model selection. A simulation study is performed to evaluate the performances of the proposed testing procedure. Empirical illustrations using insurance loss data demonstrate the relevance of the approach for model selection in supervised learning. The most positively expectation dependent predictor can then be autocalibrated to obtain its balance-corrected version that appears to be optimal with respect to Bregman, or forecast dominance.

Keywords: Expectation dependence; concentration curve; Lorenz curve; autocalibration; convex order; balance correction (search for similar items in EconPapers)
Date: 2021-01-01
New Economics Papers: this item is included in nep-ecm and nep-ias
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvad:2021021

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