Abstract
Mixture distributions have received considerable attention in life applications. This paper presents a finite Laplace mixture model with two components. We discuss the model properties and derive the parameters estimations using the method of moments and maximum likelihood estimation. We study the relationship between the parameters and the shape of the proposed distribution. The simulation study discusses the effectiveness of parameters estimations of Laplace mixture distribution.
Highlights
Laplace distribution has wide applications in various fields such as engineering, business, medicine, and others. It is known as a double exponential distribution because it is considered as two exponential distributions with additional location parameter
Mixture distributions and the problem of mixture decomposition about the identification of the constituent components and parameters dates back to 1846, but most of the reference is made due to the work of Karl Pearson in 1894 [7]
We study the effectiveness of Laplace mixture model by providing two scenarios
Summary
Laplace distribution has wide applications in various fields such as engineering, business, medicine, and others. It is known as a double exponential distribution because it is considered as two exponential distributions with additional location parameter. Mixture distributions and the problem of mixture decomposition about the identification of the constituent components and parameters dates back to 1846, but most of the reference is made due to the work of Karl Pearson in 1894 [7]. The approach taken by Pearson was to fit a univariate mixture of two normal to data through the choice of five parameters of the mixture in a way that empirical moments matched the model. Amini-Seresht and Zhang provided random comparisons of two finite-mixture models with different mixing ratios and independent variables [1]
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