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Editorial

Robust Procedures for Estimating and Testing in the Framework of Divergence Measures

by 1,2,* and 2,3
1
Department of Statistics and O. R., Faculty of Mathematics, Universidad Complutense de Madrid, 28040 Madrid, Spain
2
Interdisciplinary Mathematics Institute, Complutense University of Madrid, 28040 Madrid, Spain
3
Department of Financial and Actuarial Economics & Statistics, Faculty of Commerce and Tourism, Complutense University of Madrid, 28003 Madrid, Spain
*
Author to whom correspondence should be addressed.
Entropy 2021, 23(4), 430; https://doi.org/10.3390/e23040430
Received: 25 March 2021 / Accepted: 1 April 2021 / Published: 6 April 2021
Note: In lieu of an abstract, this is an excerpt from the first page.

The approach for estimating and testing based on divergence measures has become, in the last 30 years, a very popular technique not only in the field of statistics, but also in other areas, such as machine learning, pattern recognition, etc [...] View Full-Text
MDPI and ACS Style

Pardo, L.; Martín, N. Robust Procedures for Estimating and Testing in the Framework of Divergence Measures. Entropy 2021, 23, 430. https://doi.org/10.3390/e23040430

AMA Style

Pardo L, Martín N. Robust Procedures for Estimating and Testing in the Framework of Divergence Measures. Entropy. 2021; 23(4):430. https://doi.org/10.3390/e23040430

Chicago/Turabian Style

Pardo, Leandro, and Nirian Martín. 2021. "Robust Procedures for Estimating and Testing in the Framework of Divergence Measures" Entropy 23, no. 4: 430. https://doi.org/10.3390/e23040430

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