The Bivariate Length Biased - Power Garima Distribution Derived from Copula: Properties and Application to Environmental Data
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摘要
Bivariate distributions calculate the probabilities for simultaneous outcomes of two random variables. They are essential for understanding the relationship between two variables. This study proposed a new bivariate distribution called the bivariate length biased power Garima (BLBPG) distribution, created by combining the Farlie-Gumbel-Morgenstern (FGM) copula with a length-biased power Garima distribution. The BLBPG distribution describes lifetime bivariate data with a weak correlation between variables as a flexible alternative to bivariate lifetime distributions for modeling real-valued data in applications. The proposed distribution yielded various properties including joint conditional probability functions, reliability (survival) and hazard functions, and generating a random variable. The maximum likelihood estimation was presented to estimate the parameters of the proposed distribution and a Monte Carlo simulation study was conducted to evaluate the performance of the estimators. A practical application of the proposed bivariate distribution to analyze environmental data was also demonstrated.
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参考
Gupta RC, Akman HO. On the reliability studies of a weighted inverse Gaussian model. Journal of Statistical Planning and Inference 1995; 48(1): 69-83.
Klinjan K, Aryuyuen S. The lengthbiased power Garima distribution and its application to model lifetime data. Songklanakarin Journal of Science & Technology, 2021; 43(3): 667-76.
Lai CD. Constructions of continuous bivariate distributions. Journal of the Indian Society for Probability and Statistics 2004; 8: 21-43.
Nelsen RB. An introduction to copulas, 2nd ed., Springer, New York, NY.; 2006.
Sklar M. Fonctions de répartition à n dimensions et leurs marges. In Annales de l’ISUP 1959; 8: 229-231.
Sklar A. Random variables, joint distribution functions, and copulas. Kybernetika 1973; 9(6): 449-60.
Morgenstern D. Einfache Beispiele zweidimensionaler Verteilungen. Mitteilingsblatt für Mathematische Statistik, 1956; 8: 234-5.
Kotz S, Balakrishnan N, Johnson NL. Continuous multivariate distributions, Volume 1: Models and applications, volume 1, John Wiley & Sons; 2004.
Nadarajah S. A bivariate Pareto model for drought. Stochastic Environmental Research and Risk Assessment 2009; 23: 811-22.
Abd Elaal MK, Jarwan RS. Inference of bivariate generalized exponential distribution based on copula functions. Applied Mathematical Sciences 2017; 11(24): 1155-86.
Peres MVDO, Achcar JA, Martinez EZ. Bivariate modified Weibull distribution derived from Farlie-Gumbel-Morgenstern copula: a simulation study. Electronic Journal of Applied Statistical Analysis 2018; 11(2): 463-88.
Almetwally EM, Muhammed HZ, ElSherpieny ESA. Bivariate Weibull distribution: properties and different methods of estimation. Annals of Data Science 2020; 7: 163-93.
Zhao J, Faqiri H, Ahmad Z, Emam W, Yusuf M, Sharawy A. The Lomax-claim model: bivariate extension and applications to financial data. Complexity 2021; 2021: 1-17.
Hamed MS, Cordeiro GM, Yousof HM. A new compound Lomax model: Properties, copulas, modeling and risk analysis utilizing the negatively skewed insurance claims data. Pakistan Journal of Statistics and Operation Research 2022; 18(3): 601-31.
Abulebda M, Pandey A, Tyagi S. On bivariate inverse Lindley distribution derived from copula. Thailand Statistician 2023; 21(2): 291 304.
Saminger-Platz S, Kolesárová A, Šeliga A, Mesiar R, Klement EP. The impact on the properties of the EFGM copulas when extending this family. Fuzzy Sets and Systems 2021; 415: 1-26.
Bekrizadeh H. Generalized family of copulas: definition and properties. Thailand Statistician 2021; 19(1): 162-77.
Aryuyuen, S, Panphut W, Pudprommarat, C. Bivariate Sushila distribution based on copulas: properties, simulations, and applications. Lobachevskii Journal of Mathematics 2024; 44(11): 4592-609.
Shenbagaraja R, Rather AA, Subramanian C. On some aspects of length biased technique with real life data. Science, technology &
Development 2019; 8(9): 326-35.
Basu AP. Bivariate failure rate. Journal of the American Statistical Association1971; 66(333): 103-4.
Klinjan K, Aryuyuen S. R package for the length-biased power Garima distribution to analyze lifetime data. International Journal of Mathematics and Computer Science 2022; 17(3): 1351-62.
R Core Team et al. R: A language and environment for statistical computing. [cited 2023 November 1]. Available from: https://www.R-project.org/