https://journals.riverpublishers.com/index.php/JRSS/issue/feedJournal of Reliability and Statistical Studies2026-04-28T16:45:37+02:00Editors-in-Chiefjrss@riverpublishers.comOpen Journal Systems<p>The Journal of Reliability and Statistical Studies (JRSS) aims at the theoretical and practical aspects of Reliability and Statistics. We welcome the submission of articles, review papers and statistical studies which describe novel useful research and applications in all areas of reliability and statistics. JRSS is aimed at reliability engineers, mathematicians, statisticians and those involved in practical data analytics. The Journal concentrates on publication of interdisciplinary articles in the fields of reliability engineering, mathematical statistics, operations research, fuzzy theory, demography and population studies. We have also added a data analytics stream to support the growing amount of cross over research in this area.</p>https://journals.riverpublishers.com/index.php/JRSS/article/view/29931Stress and Strength Reliability Estimation for the Inverse Family of Distributions using Bayesian Analysis2026-03-06T19:37:36+01:00Kuldeep Singh Chauhankuldeepsinghchauhan.stat@rla.du.ac.inSachin Tomerkuldeepsinghchauhan.stat@rla.du.ac.in<p>A Bayesian model to study stress-strength reliability P=P(Y<X), which makes use of parameters in the family of the inverse distributions. For the reliability function and for the stress-strength parameter, Bayes estimators are obtained under SELF and GELF. When this is appropriate, conjugate priors will be introduced into estimators, which will be constituted using different powers of the unknown parameters. Performance of these estimators is determined by a simulation-based methodology and large numbers of bootstrap replications. The findings show that, especially in small-sample circumstances, the Bayesian estimators based on SELF perform better than those based on GELF. The performance difference closes as the sample sizes grow. The exploration of this paper displays that the inverse family can be altered for several common distributions, which have more significant practical implications when analyzing reliability.</p>2026-04-28T00:00:00+02:00Copyright (c) 2026 Journal of Reliability and Statistical Studies