scholarly journals Improved robust model selection methods for a Lévy nonparametric regression in continuous time

2019 ◽  
Vol 31 (3) ◽  
pp. 612-628 ◽  
Author(s):  
E. A. Pchelintsev ◽  
V. A. Pchelintsev ◽  
S. M. Pergamenshchikov
Keyword(s):  
Model Selection ◽  
Nonparametric Regression ◽  
Continuous Time ◽  
Selection Methods ◽  
Robust Model ◽  
Robust Model Selection

Robust model selection and the statistical classification of languages

2012 ◽  
Author(s):  
J. E. García ◽  
V. A. González-López ◽  
M. L. L. Viola ◽  
Julio Michael Stern ◽  
Marcelo De Souza Lauretto ◽  
...  
Keyword(s):  
Model Selection ◽  
Statistical Classification ◽  
Robust Model ◽  
Robust Model Selection

Robust model selection with flexible trimming

2010 ◽  
Vol 54 (12) ◽  
pp. 3300-3312 ◽  
Author(s):  
Marco Riani ◽  
Anthony C. Atkinson
Keyword(s):  
Model Selection ◽  
Robust Model ◽  
Robust Model Selection

scholarly journals Robust Model Selection Criteria Based on Pseudodistances

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 304 ◽  
Author(s):  
Aida Toma ◽  
Alex Karagrigoriou ◽  
Paschalini Trentou
Keyword(s):  
Model Selection ◽  
Regression Models ◽  
Selection Criteria ◽  
Selection Criterion ◽  
Superior Performance ◽  
Linear Regression Models ◽  
Limit Laws ◽  
Model Selection Criteria ◽  
Robust Model ◽  
Robust Model Selection

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.

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