On distance-type Gaussian estimation

Elena Castilla and Konstantinos Zografos

Journal of Multivariate Analysis, 2022, vol. 188, issue C

Abstract: 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.

Keywords: Density power divergence; Elliptically contoured distributions; Gaussian estimation; Maximum likelihood estimation; Minimum density power divergence Gaussian estimation; Robustness (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2021.104831

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