Title of article
Skew–Normal Mean–Variance Mixture of Birnbaum–Saunders Distribution and Its Associated Inference and Application
Author/Authors
Tamandi, Mostafa Department of Statistics - Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran , Negarestani, Hossein Young Researchers Society - Shahid Bahonar University of Kerman, Kerman, Iran , Jamalizadeh, Ahad Department of Statistics - Faculty of Mathematics and Computers - Shahid Bahonar University of Kerman, Kerman, Iran , Amiri, Mehdi Department of Statistics - Faculty of Basic Sciences - University of Hormozgan, Bandar abbas, Iran
Pages
27
From page
87
To page
113
Abstract
This paper presents a skew-normal mean-variance mixture based on Birnbaum-
Saunders (SNMVBS) distribution and discusses some of its key properties. The SN-
MVBS distribution can be thought as a flexible extension of the normal mean-variance
mixture based on Birnbaum-Saunders (NMVBS) distribution as it possesses one ad-
ditional shape parameter for providing more flexibility with skewness and kurtosis.
Next, we develop a computationally feasible ECM algorithm for the maximum like-
lihood estimation of the model parameters. Asymptotic standard errors of the ML
estimates are obtained through an approximation of the observed information matrix.
Finally, the usefulness of the proposed model and its fitting method are illustrated
through a Monte-Carlo simulation as well as three real-life datasets.
Keywords
Observed Information Matrix , ECM Algorithm , Birnbaum-Saunders , Ro-bustness , Scale-Shape Mixtures
Serial Year
2019
Record number
2495731
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