Pareto Randomization of the Scaling Parameter for the Gaussian Distribution

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International Journal of Informatics and Applied Mathematics
Volume:4 Issue:2
Pareto Randomization of the Scaling Parameter for the Gaussian Distribution

Pareto Randomization of the Scaling Parameter for the Gaussian Distribution

Authors : Miguel FELGUEİRAS, Rui SANTOS, Joao Paulo MARTİNS

Pages : 43-52

Doi:10.53508/ijiam.1020679

View : 43 | Download : 10

Publication Date : 2021-12-31

Article Type : Research Paper

Abstract :Gaussian distribution is a common choice when dealing with symmetric data. However, other alternatives must be considered in applications with high tail-weight. One option is the randomization of the scale parameter for the Gaussian distribution, enabling a more flexible model for the tails albeit maintaining symmetry. Although any positive random variable can be used as a random scale parameter, Pareto distribution is a suitable choice in order to increase variance and tail-weight. Therefore, the aim of this work is to study the Pareto randomization of the scale parameter for symmetric distributions, in particular for the Gaussian distribution. Estimation problem is tackled and a simulation study is discussed. Finally, an application concerning the directions chosen by ants after a stimulus is provided. The results reveal that the proposed methodology works well both on simulated and real data.
Keywords : Pareto scale mixtures, heavy tail distributions, parameters estimation, ants direction

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