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: Université Libre de Bruxelles
Thomas Verdebout: Université Libre de Bruxelles

No 2021048, LIDAM Reprints 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 compare two models. 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)
JEL-codes: C12 (search for similar items in EconPapers)
Pages: 10
Date: 2021-11-01
Note: In: Insurance: Mathematics and Economics, 2021, vol. 101, Part B, p. 163-172
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2021048

DOI: 10.1016/j.insmatheco.2021.07.008

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