A Bayesian Semiparametric Random effect model for Meta-Regression

	A Bayesian Semiparametric Random effect model for Meta-Regression
Journal of Data Science and Modeling
دوره 1، شماره 2، شهریور 2023، صفحه 205-223 اصل مقاله (497.76 K)
نوع مقاله: Research Manuscript
شناسه دیجیتال (DOI): 10.22054/jcsm.2022.69925.1032
نویسنده
Ehsan Ormoz^*
Department of Mathematics and Statistics, Mashhad Branch, Islamic Azad University
چکیده
In this paper, we will introduce a Bayesian semiparametric model concerned with both constant and coefficients. In Meta-Analysis or Meta-Regression, we usually use a parametric family. However, lately the increasing tendency to use Bayesian nonparametric and semiparametric models, entered this area too. On the other hand, although we have some works on Bayesian nonparametric or semiparametric models, they just focus on intercept and do not pay much attention to regressor coefficient(s). We also would check the efficiency of the proposed model via simulation and give an illustrating example.
کلیدواژه‌ها
Meta-Analysis؛ Meta-Regression؛ Dirichlet process؛ Bayesian Model Selection؛ Gibbs Sampling

آمار تعداد مشاهده مقاله: 123 تعداد دریافت فایل اصل مقاله: 93

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب