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Open AccessArticle

Robust Model Selection Criteria Based on Pseudodistances

Department of Applied Mathematics, Bucharest University of Economic Studies, 010164 Bucharest, Romania
“Gh. Mihoc - C. Iacob” Institute of Mathematical Statistics and Applied Mathematics, Romanian Academy, 010164 Bucharest, Romania
Department of Statistics and Actuarial-Financial Mathematics, Lab of Statistics and Data Analysis, University of the Aegean, 83200 Karlovasi, Greece
Author to whom correspondence should be addressed.
Entropy 2020, 22(3), 304;
Received: 17 February 2020 / Revised: 2 March 2020 / Accepted: 3 March 2020 / Published: 6 March 2020
In this paper, we introduce a new class of robust model selection criteria. These criteria are defined by estimators of the expected overall discrepancy using pseudodistances and the minimum pseudodistance principle. Theoretical properties of these criteria are proved, namely asymptotic unbiasedness, robustness, consistency, as well as the limit laws. The case of the linear regression models is studied and a specific pseudodistance based criterion is proposed. Monte Carlo simulations and applications for real data are presented in order to exemplify the performance of the new methodology. These examples show that the new selection criterion for regression models is a good competitor of some well known criteria and may have superior performance, especially in the case of small and contaminated samples. View Full-Text
Keywords: model selection; minimum pseudodistance estimation; Robustness model selection; minimum pseudodistance estimation; Robustness
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MDPI and ACS Style

Toma, A.; Karagrigoriou, A.; Trentou, P. Robust Model Selection Criteria Based on Pseudodistances. Entropy 2020, 22, 304.

AMA Style

Toma A, Karagrigoriou A, Trentou P. Robust Model Selection Criteria Based on Pseudodistances. Entropy. 2020; 22(3):304.

Chicago/Turabian Style

Toma, Aida; Karagrigoriou, Alex; Trentou, Paschalini. 2020. "Robust Model Selection Criteria Based on Pseudodistances" Entropy 22, no. 3: 304.

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