2026-08-20T17:00:26Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/729642023-08-26T05:12:49Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Felipe Ortega, Ángel
author
Jaenada Malagón, María
author
Miranda Menéndez, Pedro
author
Pardo Llorente, Leandro
2023-06-22T12:38:05Z
2023-06-22T12:38:05Z
2023-03-17
2227-7390
10.3390/math11061480
https://hdl.handle.net/20.500.14352/72964
https://doi.org/10.3390/math11061480
https://www.mdpi.com/2227-7390/11/6/1480
In this paper, we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. In addition, we examine it robustness through its influence function analysis. Restricted estimators are required in many practical situations, such as testing composite null hypotheses, and we provide in this case constrained estimators to inherent restrictions of the underlying distribution. Furthermore, we derive robust Rao-type test statistics based on the MDPDGE for testing a simple null hypothesis, and we deduce explicit expressions for some main important distributions. Finally, we empirically evaluate the efficiency and robustness of the method through a simulation study
eng
Atribución 3.0 España
Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
journal article
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mathematics-11-01480-v2.pdf
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mathematics-11-01480-v2.pdf.txt