Abstract
This article presents a bivariate extension of the Teissier distribution, whose univariate marginal distributions belong to the exponentiated Teissier family. Analytic expressions for the different statistical quantities such as conditional distribution, joint moments, and quantile function are explicitly derived. For the proposed distribution, the concepts of reliability and dependence measures are also explored in details. Both the maximum likelihood technique and the Bayesian approach are utilised in the process of parameter estimation for the proposed distribution with unknown parameters. Several numerical experiments are reported to study the performance of the classical and Bayes estimators for varying sample size. Finally, a bivariate data is fitted using the proposed distribution to show its applicability over the bivariate exponential, Rayleigh, and linear exponential distributions in real-life situations.
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Funding
Science and Engineering Research Board, Department of Science & Technology, Govt. of India, under the scheme Early Career Dr. Vikas Kumar Sharma greatly acknowledges the financial support from Research Award (ECR/2017/002416). Dr. Sharma acknowledges Banaras Hindu University, Varanasi, India for providing financial support as seed grant under the Institute of Eminence Scheme (Scheme no. Dev. 6031).
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Sharma, V.K., Singh, S.V. & Pathak, A.K. A Bivariate Teissier Distribution: Properties, Bayes Estimation and Application. Sankhya A 86, 67–92 (2024). https://doi.org/10.1007/s13171-023-00314-w
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DOI: https://doi.org/10.1007/s13171-023-00314-w
Keywords
- Teissier distribution
- Bivariate distribution
- Measures of dependence
- Mean residual life
- Quantile function
- Maximum likelihood estimator
- Bayes estimator