On Bivariate Inverse Lindley Distribution Derived From Copula

Authors

  • Mohammed Abulebda Department of Statistics, Central University of Rajasthan, Rajasthan, India
  • Arvind Pandey Department of Statistics, Central University of Rajasthan, Rajasthan, India
  • Shikhar Tyagi Department of Statistics, Central University of Rajasthan, Rajasthan, India

Keywords:

Bivariate inverse Lindley distribution, copula, Farlie-Gumbel-Morgenstern copula, maximum likelihood estimate

Abstract

In the last few decades, copula distribution has become one of the most popular methods to construct bivariate distributions in literature. This paper introduces a new bivariate Inverse Lindley distribution based on the Farlie-Gumbel-Morgenstern (FGM) copula. We study essential mathematical properties and it’s application. We have been using the conditional copula distribution method to generate random numbers. Estimation of the parameters for bivariate Inverse Lindley distribution is obtained through maximum likelihood estimation. An example of a real data set is introduced to illustrate the proposed model.

References

Abd Elaal MK, Jarwan RS. Inference of bivariate generalized exponential distribution based on copula functions. Appl Math Sci. 2017; 11(24): 1155-1186.

Achcar JA, Moala FA, Tarumoto MH, Coladello LF. A bivariate generalized exponential distribution derived from copula functions in the presence of censored data and covariates. Pesquisa Operacional. 2015; 35: 165-186.

Almetwally EM, Muhammed HZ, El-Sherpieny ES. Bivariate Weibull distribution: properties and different methods of estimation. Ann Data Sci. 2020; 7(1): 163-193.

Alley WM. The Palmer drought severity index: limitations and assumptions. J Appl Meteorol Clim. 1984; 23(7): 1100-1109.

Balakrishnan N, Lai CD. Continuous Bivariate Distributions. New York: Springer Science & Business Media; 2009.

Basu AP. Bivariate failure rate. J Amer Stat Assoc. 1971; 66, 103-104.

Bhattacharjee S, Misra SK. Some aging properties of Weibull models. Electron J Appl Stat. 2016; 9(2): 297-307.

Coelho-Barros EA, Achcar JA, Mazucheli J. Bivariate Weibull distributions derived from copula functions in the presence of cure fraction and censored data. J Data Sci. 2016; 14: 295-316.

Guo L, Gui W. Bayesian and classical estimation of the inverse Pareto distribution and its application to strength-stress models. Am J Math-S. 2018; 37(1): 80-92.

Holland PW, Wang YJ. Dependence function for continuous bivariate densities. Commun Stat Theory. 1987; 16(3): 863-876.

Johnson NL, Kotz S. A vector multivariate hazard rate. J Multivariate Anal. 1975; 5(1): 53-66.

Kundu D. Bivariate sinh-normal distribution and a related model. Braz J Prob Stat. 2015; 29(3): 590-607.

Kundu D, Gupta RD. Absolute continuous bivariate generalized exponential distribution. AStA Advances in Statistical Analysis. 2011; 95(2): 169-185.

Kundu D, Gupta RC. On Bivariate BirnbaumSaunders Distribution. Am J Math-S. 2017; 36(1): 21-33.

Morgenstern D. Einfache beispiele zweidimensionaler verteilungen. Mitteilingsblatt fur Mathematische Statistik. 1956; 8: 234-235.

Nadarajah S. A bivariate gamma model for drought. Water Resour Res. 2007; 43(8), http://doi.org/10.1029/2006WR005641.

Nadarajah S. A bivariate pareto model for drought. Stoch Env Res Risk A. 2009; 23(6): 811-822.

Nelsen RB. An introduction to copulas, 2nd ed. New York: Springer Science & Business Media; 2006.

Oakes D. Bivariate survival models induced by frailties. J Am Stat Assoc. 1989; 84(406): 487-493.

Pathak AK, Vellaisamy P. A bivariate generalized linear exponential distribution: properties and estimation. Commun Stat Simulat. 2020; 10: 1-21.

Peres MV, Achcar JA, Martinez EZ. Bivariate modified Weibull distribution derived from FarlieGumbel-Morgenstern copula: a simulation study. Electron J Appl Stat. 2018; 11(2): 463-488.

Rinne H. The Weibull distribution: a handbook. CRC press, Germany: Justus Liebig University;

Sharma VK, Singh SK, Singh U, Agiwal V. The inverse Lindley distribution: a stress-strength reliability model with application to head and neck cancer data. J Ind Prod Eng. 2015; 32(3): 162-173.

Sklar M. Fonctions de repartition an dimensions et leurs marges. Publ inst statist univ Paris. 1959; 8: 229-231.

Vincent Raja A, Gopalakrishnan A. On the analysis of bivariate lifetime data: Some models and applications. PhD [dissertation]. Cochin University of Science and Technology; 2017.

Yevjevich VM. Objective approach to definitions and investigations of continental hydrologic droughts. PhD [dissertation]. Colorado State University. Libraries; 1967.

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Published

2023-03-29

How to Cite

Abulebda, M. ., Pandey, A. ., & Tyagi, S. . (2023). On Bivariate Inverse Lindley Distribution Derived From Copula. Thailand Statistician, 21(2), 291–304. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/249002

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