A Bivariate Modified Odd Generalized Exponential Linear Failure Rate Distribution
International Journal of Sciences: Basic and Applied Research • 2021
Publication Information
Authors
Mervet Mahdy Mai Mahrous
Keywords
Not Available
Journal
International Journal of Sciences: Basic and Applied Research
Publisher
Dr Mohammad Nassar
Volume
Not Available
Issue
Not Available
Pages
24
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
In this paper we introduce a bivariate modified odd generalized exponential linear failure rate (BMOGELFR)
distribution. The cumulative distribution function of this bivariate model has absolutely continuous and singular
parts. Representations for the cumulative and density functions are presented and properties such as marginal,
conditional distributions and some reliability measures, the joint reliability function, joint hazard rate, and joint
reversed (hazard) function. The estimation of the parameters by maximum likelihood is discussed and the Fisher
information matrix is determined. An applications to real data is carried out to illustrate that the new distribution
is more flexible and e¤ective than other popular distributions in modeling lifetime data. Finally, some simulations
are presented to verify the performance of the direct maximum-likelihood estimation.
distribution. The cumulative distribution function of this bivariate model has absolutely continuous and singular
parts. Representations for the cumulative and density functions are presented and properties such as marginal,
conditional distributions and some reliability measures, the joint reliability function, joint hazard rate, and joint
reversed (hazard) function. The estimation of the parameters by maximum likelihood is discussed and the Fisher
information matrix is determined. An applications to real data is carried out to illustrate that the new distribution
is more flexible and e¤ective than other popular distributions in modeling lifetime data. Finally, some simulations
are presented to verify the performance of the direct maximum-likelihood estimation.
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