Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/12568
Title: Modeling Bivariate Data Using Linear Exponential and Weibull Distributions as Marginals
Authors: Arshad, Mohd.
Keywords: Bivariate generalized linear Weibull distribution;generalized linear exponential distribution;inference;MCMC;measures of association;Weibull distribution
Issue Date: 2023
Publisher: De Gruyter Open Ltd
Citation: Arshad, M., Pathak, A. K., Azhad, Q. J., & Khetan, M. (2023). Modeling Bivariate Data Using Linear Exponential and Weibull Distributions as Marginals. Mathematica Slovaca. Scopus. https://doi.org/10.1515/ms-2023-0079
Abstract: Modeling bivariate data with different marginals is an important problem and have numerous applications in diverse disciplines. This paper introduces a new family of bivariate generalized linear exponential Weibull distribution having generalized linear and exponentiated Weibull distributions as marginals. Some important quantities like conditional distributions, conditional moments, product moments and bivariate quantile functions are derived. Concepts of reliability and measures of dependence are also discussed. The methods of maximum likelihood and Bayesian estimation are considered to estimate model parameters. Monte Carlo simulation experiments are performed to demonstrate the performance of the estimators. Finally, a real data application is also discussed to demonstrate the usefulness of the proposed distribution in real-life situations. © 2023 Mathematical Institute Slovak Academy of Sciences.
URI: https://doi.org/10.1515/ms-2023-0079
https://dspace.iiti.ac.in/handle/123456789/12568
ISSN: 0139-9918
Type of Material: Journal Article
Appears in Collections:Department of Mathematics

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric Badge: