Finite Mixture Modeling via Skew-Laplace Birnbaum–Saunders Distribution

Abstract

Finite mixture model is a widely acknowledged model-based clustering method for analyzing data. In this paper, a new finite mixture model via an extension of Birnbaum–Saunders distribution is introduced. The new mixture model provide a useful generalization of the heavy-tailed lifetime model since the mixing components cover both skewness and kurtosis. Some properties and characteristics of the model are derived and an expectation and maximization (EM)-type algorithm is developed to compute maximum likelihood estimates. The asymptotic standard errors of the parameter estimates are obtained via offering an information-based approach. Finally, the performance of the methodology is illustrated by considering both simulated and real datasets.

Keywords

• Birnbaum–Saunders distribution
• Normal mean-variance mixture model
• Skew-Laplace distribution
• Finite mixture model
• ECM algorithm