Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling (original) (raw)

Abstract

In this paper, we propose mixtures of skew Laplace normal distributions to model both skewness and heavy-tailedness in the heterogeneous data set as an alternative to mixtures of skew Studentt-normal distributions. We give the EM algorithm to obtain the maximum likelihood estimators for the parameters of interest. We also analyze the mixture regression model based on the skew Laplace normal distribution and provide the maximum likelihood estimators of the parameters using the EM algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples.

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