The Alpha-Beta Skew Generalized t Distribution: Properties and Applications

Fazlollah Lak,Hamid Esmaeili,Mohammad Esmaeil Dehghan Monfared,Morad Alizadeh

doi:10.18187/pjsor.v15i3.2404

Fazlollah Lak, Hamid Esmaeili + Show 2 more

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https://doi.org/10.18187/pjsor.v15i3.2404

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Abstract

n this paper, we propose a new distribution, namely alpha-beta-skew generalized tdistribution. The proposed distribution is really flxible and includes as special modelsÂ some important distributions like Normal, t-student, Cauchy and etc as its marginalÂ component distributions. It features a probability density function with up to threeÂ modes. The moment generating function as well as the main moments are provided.Â Inference is based on a usual maximum-likelihood estimation approach and a smallÂ Monte Carlo simulation is conducted for studying the asymptotic properties of theÂ maximum-likelihood estimate. The usefulness of the new model is illustrated in a realÂ data

Highlights

The normality assumption is one of the conditions in statistical procedures.in most cases of real-life problems, the normality assumption has not established and non-normal distributions for modeling data sets having skewness and/or kurtosis are more prevalent, see for example (Tiku and eatl,2011) and (Celik and eatl, 2015).the construction of the non-normal distributions has been an enormous interest and attracted the attention of researchers.The Generalized t distribution was defined by (McDonald and Newey,1998) to develop a partially adaptive M-regression procedure
The aim of this paper is to introduce a new family of distributions as an extension of alpha skew generalized t distribution with the pdf (3)
Aplication 5.1 In this study, we model roller data by using ABSGT distribution

Summary

Introduction

The normality assumption is one of the conditions in statistical procedures.in most cases of real-life problems, the normality assumption has not established and non-normal distributions for modeling data sets having skewness and/or kurtosis are more prevalent, see for example (Tiku and eatl,2011) and (Celik and eatl , 2015).the construction of the non-normal distributions has been an enormous interest and attracted the attention of researchers.The Generalized t distribution was defined by (McDonald and Newey,1998) to develop a partially adaptive M-regression procedure. The normality assumption is one of the conditions in statistical procedures. In most cases of real-life problems, the normality assumption has not established and non-normal distributions for modeling data sets having skewness and/or kurtosis are more prevalent, see for example (Tiku and eatl,2011) and (Celik and eatl , 2015). The construction of the non-normal distributions has been an enormous interest and attracted the attention of researchers. The Generalized t distribution was defined by (McDonald and Newey,1998) to develop a partially adaptive M-regression procedure. The procedure includes many other estimation methods such as least squares, least absolute deviation and Lp. The procedure includes many other estimation methods such as least squares, least absolute deviation and Lp It has been followed up more recently by (Theodossiou,1998) and (Arslan and Genç,2003)

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