TY - JOUR
T1 - On the consistency and the robustness in model selection criteria
AU - Kurata, Sumito
AU - Hamada, Etsuo
N1 - Funding Information:
This work was partly supported by JSPS KAKENHI Grant Number 16J04579 and 18K03413. The authors would like to express their gratitude to the reviewer and the editor in chief for their valuable comments, which have considerably improved the earlier version of the article.
Publisher Copyright:
© 2019 Taylor & Francis Group, LLC.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
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U2 - 10.1080/03610926.2019.1615093
DO - 10.1080/03610926.2019.1615093
M3 - Article
AN - SCOPUS:85091480034
VL - 49
SP - 5175
EP - 5195
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
SN - 0361-0926
IS - 21
ER -