In this paper, we develop a new procedure for estimating the parameters of a model by combining Zhang's (2019) recent Gaussian estimator and the minimum density power divergence estimators of Basu et al. (1998). The proposed estimator is called the Minimum Density Power Divergence Gaussian Estimator (MDPDGE). The consistency and asymptotic normality of the MDPDGE are proved. The MDPDGE is applied to some classical univariate distributions and it is also investigated for the family of elliptically contoured distributions. A numerical study illustrates the robustness of the proposed estimator. (C) 2021 Elsevier Inc. All rights reserved.
Density power divergenceElliptically contoured distributionsGaussian estimationMaximum likelihood estimationMinimum density power divergenceRobustnessDENSITY POWER DIVERGENCEROBUSTREGRESSIONMODELS
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Elsevier
