On the consistency and the robustness in model selection criteria

Sumito Kurata, Etsuo Hamada

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In the model selection problem, the consistency of the selection criterion has been often discussed. This paper derives a family of criteria based on a robust statistical divergence family by using a generalized Bayesian procedure. The proposed family can achieve both consistency and robustness at the same time since it has good performance with respect to contamination by outliers under appropriate circumstances. We show the selection accuracy of the proposed criterion family compared with the conventional methods through numerical experiments.

Original languageEnglish
Pages (from-to)5175-5195
Number of pages21
JournalCommunications in Statistics - Theory and Methods
Issue number21
Publication statusPublished - Nov 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


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