A note on the Fisher information matrix for the skew-generalized-normal model

Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2013

A note on the Fisher information matrix for the skew-generalized-normal model

Identification data

Identifier: RP:2392

Handle: https://hdl.handle.net/20.500.11797/RP2392

Authors:
Salinas, Hugo S.Gómez, Héctor W.Arellano-Valle, Reinaldo B.

Abstract:
In this paper, the exact form of the Fisher information matrix for the skew-generalized normal (SGN) distribution is determined. The existence of singularity problems of this matrix for the skew-normal and normal particular cases is investigated. Special attention is given to the asymptotic properties of the MLEs under the skew-normality hypothesis.
Others:

URV's Author/s: Salinas, Hugo S. Gómez, Héctor W. Arellano-Valle, Reinaldo B.
Keywords: Asymptotic distribution, Kurtosis, maximum likelihood estimation, singular information matrix, Skewness
Abstract: In this paper, the exact form of the Fisher information matrix for the skew-generalized normal (SGN) distribution is determined. The existence of singularity problems of this matrix for the skew-normal and normal particular cases is investigated. Special attention is given to the asymptotic properties of the MLEs under the skew-normality hypothesis.
Journal publication year: 2013
Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

Keywords:

Asymptotic distribution, Kurtosis, maximum likelihood estimation, singular information matrix, Skewness
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