On the Finite Mixture Modelling via Normal Mean-variance Birnbaum-Saunders Distribution

Abstract

Abstract. This paper presents a new finite mixture model using the normal meanvariance mixture of Birnbaum-Saunders distribution. The proposed model is multimodal with wider ranges of skewness and kurtosis. Moreover, it is useful for modeling highly asymmetric data in various theoretical and applied statistical problems. The maximum likelihood estimates of the parameters of the model are computed iteratively by feasible EM algorithm. To illustrate the finite sample properties and performance of the estimators, we conduct a simulation study and illustrate the usefulness of the new model by analyzing a real dataset.

Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributions

“…Upon the normality and/or non-normality assumptions for the mixing components, various FM models have recently been introduced for modeling heterogeneous data. They have widely been employed in scientific studies such as genetics, image processing, medicine, economics and astronomy, for example in Wang et al (2018); Punzo et al (2018); Sugasawa et al (2018); Naderi et al (2017aNaderi et al ( , 2019; Tomarchio and Punzo (2019); Morris et al (2019) to name a few recently published papers.…”