RT Journal Article
T1 Restricted distance-type Gaussian estimators based on density power divergence and their aplications in hypothesis testing
A1 Felipe Ortega, Ángel
A1 Jaenada Malagón, María
A1 Miranda Menéndez, Pedro
A1 Pardo Llorente, Leandro
AB 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
PB MDPI
SN 2227-7390
YR 2023
FD 2023-03-17
LK https://hdl.handle.net/20.500.14352/72964
UL https://hdl.handle.net/20.500.14352/72964
LA eng
NO Ministerio de Ciencia e Innovación (MICINN)
DS Docta Complutense
RD 21 abr 2025