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Bayesian Analysis of the Weighted Marshall-Olkin Bivariate Exponential Model | ||
| Journal of Data Science and Modeling | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 25 فروردین 1405 اصل مقاله (1.31 M) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2026.85970.1068 | ||
| نویسندگان | ||
| Ali Sakhaei؛ Iman Makhdoom* | ||
| Department of Statistics, Payame Noor University (PNU), Tehran, Iran | ||
| چکیده | ||
| The Weighted Marshall-Olkin Bivariate Exponential (WMOBE) distribution was first proposed by Jamalizadeh and Kundu (2013), who examined its different characteristics and properties. Bayesian estimation of the model parameters is carried out using both the squared error loss (SEL) function, which is symmetric, and the linear-exponential (LINEX) loss function, which is asymmetric. These estimators are derived under both informative and non-informative gamma priors. Given the complexity of the four-parameters model, explicit analytical solutions for the Bayesian estimators are not attainable, making it necessary to employ the Gibbs sampling procedure. Markov Chain Monte Carlo (MCMC) methods are widely utilized to compute and implement these estimates. Furthermore, the convergence behavior of the Markov chain toward a stationary distribution is carefully analyzed. Credible intervals, particularly the highest posterior density (HPD) intervals for the unknown parameters, are also constructed. To assess and compare the effectiveness of these estimation approaches, Monte Carlo simulations are performed. Finally, the methodology is applied to a real-world dataset for illustrative purposes. | ||
| کلیدواژهها | ||
| Bayesian estimation؛ Markov Chain Monte Carlo؛ Gibss sampling؛ Weighted Marshall-Olkin Bivariate Exponential distribution | ||
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