The New Bivariate Odd Generalized Exponential Modified Weibull Distribution with Application
• 2023
Publication Information
Authors
Mervat Mahdy;Asmaa Abdeltfatah; Dina S. El-telbany
Keywords
Bivariate distribution, Maximum likelihood estimation,
Modified Weibull distribution, Odd Generalized Exponential,
Simulation.
Journal
Not Available
Publisher
Not Available
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
In this paper, we introduce a new bivariate odd generalized exponential
modified Weibull (BOGEMW) distribution. This model includes some
other well-known models such as the Weibull, generalized Weibull,
exponential Weibull, Odd Generalized Exponential, and Modified Weibull
distribution. The model introduced here is of Marshall and Olkin (1967)
type. The marginals of the new bivariate distribution have odd generalized
exponential modified Weibull distribution which proposed by Abdelall
(2016). The joint probability density function and the joint cumulative
distribution function are given in closed forms. Several properties of the
new distribution are studied. The method of maximum likelihood is used
for estimating the model parameters and the observed Fisher's information
matrix is derived. We also conduct Monte Carlo simulation experiments
to assess the finite sample properties of the proposed estimation methods.
We prove empirically the importance and flexibility of the new model in
modeling various types of data.
modified Weibull (BOGEMW) distribution. This model includes some
other well-known models such as the Weibull, generalized Weibull,
exponential Weibull, Odd Generalized Exponential, and Modified Weibull
distribution. The model introduced here is of Marshall and Olkin (1967)
type. The marginals of the new bivariate distribution have odd generalized
exponential modified Weibull distribution which proposed by Abdelall
(2016). The joint probability density function and the joint cumulative
distribution function are given in closed forms. Several properties of the
new distribution are studied. The method of maximum likelihood is used
for estimating the model parameters and the observed Fisher's information
matrix is derived. We also conduct Monte Carlo simulation experiments
to assess the finite sample properties of the proposed estimation methods.
We prove empirically the importance and flexibility of the new model in
modeling various types of data.
Staff Members - Benha University