Asian Journal of Probability and Statistics https://journalajpas.com/index.php/AJPAS <p style="text-align: justify;"><strong>Asian Journal of Probability and Statistics (ISSN: 2582-0230)</strong> aims to publish high-quality papers (<a href="/index.php/AJPAS/general-guideline-for-authors">Click here for Types of paper</a>) in all areas of ‘Probability and Statistics’. By not excluding papers on the basis of novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open access INTERNATIONAL journal.</p> en-US contact@journalajpas.com (Asian Journal of Probability and Statistics) contact@journalajpas.com (Asian Journal of Probability and Statistics) Mon, 25 Jan 2021 06:14:29 +0000 OJS 3.1.1.4 http://blogs.law.harvard.edu/tech/rss 60 A Simulation Study of Bayesian Estimator for Seemingly Unrelated Regression under Different Distributional Assumptions https://journalajpas.com/index.php/AJPAS/article/view/30251 <p>This paper presents Bayesian analysis of Seemingly Unrelated Regression (SUR) model. An independent prior for parameters was used. The Bayesian method was compared with classical method of estimation to know the most efficient estimator under different distributional assumptions through a simulation study. In order to facilitate comparison among these estimators, Mean Squared Error (MSE) was considered as a criterion. Furthermore, based on the simulation, it was deduced that MSE of the Bayesian estimator is smaller than all the classical methods of estimation for SUR model while Normal distribution was considered as an ideal distribution&nbsp; in generation of disturbances in any simulation study.</p> Ojo O. Oluwadare, Owonipa R. Oluremi, Enesi O. Lateifat ##submission.copyrightStatement## https://journalajpas.com/index.php/AJPAS/article/view/30251 Mon, 25 Jan 2021 00:00:00 +0000 Prediction of Cases of Infection and Deaths Caused by COVID-19 in Mexico through the Construction of Probabilistic Models under Health Conditions in 2020 https://journalajpas.com/index.php/AJPAS/article/view/30252 <p>In the present research work, two probabilistic models are constructed, which are exponential regression and negative binomial regression. The first one refers to the number of positive cases of being infected by COVID-19. The second one refers to deaths. It was possible to estimate the dynamics of the phenomenon with both instruments, resulting in the presence of more than 106 thousand positive cases of COVID - 19, with an approximation of more than 9 thousand deaths, all of this, in approximately 4 months. In the first case, these were the results, which when updated with data issued by the federal government's health sector in November, changed the contagion scenarios and the estimates of deaths from covid-19.</p> Juan Bacilio Guerrero Escamilla, Sócrates López Pérez, Yamile Rangel Martínez ##submission.copyrightStatement## https://journalajpas.com/index.php/AJPAS/article/view/30252 Wed, 27 Jan 2021 00:00:00 +0000