A Bimodal Extension of the Exponential Distribution with Applications in Risk Theory
There are some generalizations of the classical exponential distribution in the statistical literature that have proven to be helpful in numerous scenarios. Some of these distributions are the families of distributions that were proposed by Marshall and Olkin and Gupta. The disadvantage of these mod...
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doaj-d762cd94e521414892381c83bd92850f2021-04-14T23:01:33ZengMDPI AGSymmetry2073-89942021-04-011367967910.3390/sym13040679A Bimodal Extension of the Exponential Distribution with Applications in Risk TheoryJimmy Reyes0Emilio Gómez-Déniz1Héctor W. Gómez2Enrique Calderín-Ojeda3Department of Mathematics, Faculty of Basic Science, University of Antofagasta, Antofagasta 02800, ChileDepartment of Quantitative Methods and Institute of Tourism and Sustainable Economic Development (TIDES), University of Las Palmas de Gran Canaria, 35017 Las Palmas, SpainDepartment of Mathematics, Faculty of Basic Science, University of Antofagasta, Antofagasta 02800, ChileCentre for Actuarial Studies, Department of Economics, University of Melbourne, Parkville, VIC 3010, AustraliaThere are some generalizations of the classical exponential distribution in the statistical literature that have proven to be helpful in numerous scenarios. Some of these distributions are the families of distributions that were proposed by Marshall and Olkin and Gupta. The disadvantage of these models is the impossibility of fitting data of a bimodal nature of incorporating covariates in the model in a simple way. Some empirical datasets with positive support, such as losses in insurance portfolios, show an excess of zero values and bimodality. For these cases, classical distributions, such as exponential, gamma, Weibull, or inverse Gaussian, to name a few, are unable to explain data of this nature. This paper attempts to fill this gap in the literature by introducing a family of distributions that can be unimodal or bimodal and nests the exponential distribution. Some of its more relevant properties, including moments, kurtosis, Fisher’s asymmetric coefficient, and several estimation methods, are illustrated. Different results that are related to finance and insurance, such as hazard rate function, limited expected value, and the integrated tail distribution, among other measures, are derived. Because of the simplicity of the mean of this distribution, a regression model is also derived. Finally, examples that are based on actuarial data are used to compare this new family with the exponential distribution.https://www.mdpi.com/2073-8994/13/4/679bimodalcovariatesexponential distributionfitlife insurance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jimmy Reyes Emilio Gómez-Déniz Héctor W. Gómez Enrique Calderín-Ojeda |
spellingShingle |
Jimmy Reyes Emilio Gómez-Déniz Héctor W. Gómez Enrique Calderín-Ojeda A Bimodal Extension of the Exponential Distribution with Applications in Risk Theory Symmetry bimodal covariates exponential distribution fit life insurance |
author_facet |
Jimmy Reyes Emilio Gómez-Déniz Héctor W. Gómez Enrique Calderín-Ojeda |
author_sort |
Jimmy Reyes |
title |
A Bimodal Extension of the Exponential Distribution with Applications in Risk Theory |
title_short |
A Bimodal Extension of the Exponential Distribution with Applications in Risk Theory |
title_full |
A Bimodal Extension of the Exponential Distribution with Applications in Risk Theory |
title_fullStr |
A Bimodal Extension of the Exponential Distribution with Applications in Risk Theory |
title_full_unstemmed |
A Bimodal Extension of the Exponential Distribution with Applications in Risk Theory |
title_sort |
bimodal extension of the exponential distribution with applications in risk theory |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2021-04-01 |
description |
There are some generalizations of the classical exponential distribution in the statistical literature that have proven to be helpful in numerous scenarios. Some of these distributions are the families of distributions that were proposed by Marshall and Olkin and Gupta. The disadvantage of these models is the impossibility of fitting data of a bimodal nature of incorporating covariates in the model in a simple way. Some empirical datasets with positive support, such as losses in insurance portfolios, show an excess of zero values and bimodality. For these cases, classical distributions, such as exponential, gamma, Weibull, or inverse Gaussian, to name a few, are unable to explain data of this nature. This paper attempts to fill this gap in the literature by introducing a family of distributions that can be unimodal or bimodal and nests the exponential distribution. Some of its more relevant properties, including moments, kurtosis, Fisher’s asymmetric coefficient, and several estimation methods, are illustrated. Different results that are related to finance and insurance, such as hazard rate function, limited expected value, and the integrated tail distribution, among other measures, are derived. Because of the simplicity of the mean of this distribution, a regression model is also derived. Finally, examples that are based on actuarial data are used to compare this new family with the exponential distribution. |
topic |
bimodal covariates exponential distribution fit life insurance |
url |
https://www.mdpi.com/2073-8994/13/4/679 |
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