http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=Fawzy_et_al_2025aFawzy et al 2025a - Revision history2026-05-11T11:34:37ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10http://www.colloquiam.com/wd/index.php?title=Fawzy_et_al_2025a&diff=325805&oldid=prevScipediacontent: Scipediacontent moved page Review 330296222446 to Fawzy et al 2025a2025-10-29T09:55:08Z<p>Scipediacontent moved page <a href="/public/Review_330296222446" class="mw-redirect" title="Review 330296222446">Review 330296222446</a> to <a href="/public/Fawzy_et_al_2025a" title="Fawzy et al 2025a">Fawzy et al 2025a</a></p>
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</td></tr></table>Scipediacontenthttp://www.colloquiam.com/wd/index.php?title=Fawzy_et_al_2025a&diff=325757&oldid=prevJSanchez: JSanchez moved page Draft Sanchez Pinedo 534765952 to Review 3302962224462025-10-29T09:13:04Z<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_534765952" class="mw-redirect" title="Draft Sanchez Pinedo 534765952">Draft Sanchez Pinedo 534765952</a> to <a href="/public/Review_330296222446" class="mw-redirect" title="Review 330296222446">Review 330296222446</a></p>
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</td></tr></table>JSanchezhttp://www.colloquiam.com/wd/index.php?title=Fawzy_et_al_2025a&diff=325756&oldid=prevJSanchez: Created page with " == Abstract == <p>This paper aims to estimate the unknown parameters for the exponentiated Rayleigh distribution using Type-I hybrid censored data under a step-stress model...."2025-10-29T09:12:36Z<p>Created page with " == Abstract == <p>This paper aims to estimate the unknown parameters for the exponentiated Rayleigh distribution using Type-I hybrid censored data under a step-stress model...."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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<p>This paper aims to estimate the unknown parameters for the exponentiated Rayleigh distribution using Type-I hybrid censored data under a step-stress model. The maximum likelihood and Bayes methods estimate the parameters and acceleration factor. The parameters&rsquo; approximate confidence intervals are created. The Bayes estimates of the parameters for the squared error and linear exponential loss functions are computed using the Markov Chain Monte Carlo (MCMC) method. Finally, we perform a simulation study to evaluate the effectiveness of the proposed estimators. We provide a real-life data example (Strength data measurement in GPA, for single and impregnated carbon fibers) to explain the obtained results.OPEN ACCESS Received: 15/04/2025 Accepted: 21/08/2025 Published: 27/10/2025</p><br />
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== Document ==<br />
<pdf>Media:Draft_Sanchez Pinedo_534765952-3791-document.pdf</pdf></div>JSanchez