A novel mixture model using the multivariate normal mean–variance mixture of Birnbaum–Saunders distributions and its application to extrasolar planets

Mehrdad Naderi, Wen-Liang Hung, Tsung-I Lin and Ahad Jamalizadeh

Journal of Multivariate Analysis, 2019, vol. 171, issue C, 126-138

Abstract: This paper presents a new finite mixture model based on the multivariate normal mean–variance mixture of Birnbaum–Saunders (NMVBS) distribution. We develop a computationally analytical EM algorithm for model fitting. Due to the dependence of this algorithm on initial values and the number of mixing components, a learning-based EM algorithm and an extended variant are proposed. Numerical simulations show that the proposed algorithms allow for better clustering performance and classification accuracy than some competing approaches. The effectiveness and prominence of the proposed methodology are also shown through an application to an extrasolar planet dataset.

Keywords: Birnbaum–Saunders distribution; EM algorithm; GH distribution; Normal mean–variance mixture (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.jmva.2018.11.015

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