http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=Mohammed_et_al_2025a 2026-05-11T07:26:09Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 http://www.colloquiam.com/wd/index.php?title=Mohammed_et_al_2025a&diff=323349&oldid=prev 2025-08-14T09:16:58Z

<p>Scipediacontent moved page <a href="/public/Review_242242859862" class="mw-redirect" title="Review 242242859862">Review 242242859862</a> to <a href="/public/Mohammed_et_al_2025a" title="Mohammed et al 2025a">Mohammed et al 2025a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 09:16, 14 August 2025</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

Scipediacontent http://www.colloquiam.com/wd/index.php?title=Mohammed_et_al_2025a&diff=323335&oldid=prev 2025-08-14T08:58:27Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_618047731" class="mw-redirect" title="Draft Sanchez Pinedo 618047731">Draft Sanchez Pinedo 618047731</a> to <a href="/public/Review_242242859862" class="mw-redirect" title="Review 242242859862">Review 242242859862</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 08:58, 14 August 2025</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

JSanchez http://www.colloquiam.com/wd/index.php?title=Mohammed_et_al_2025a&diff=323334&oldid=prev 2025-08-14T08:58:25Z

<p>Created page with &quot; == Abstract == &lt;p&gt;This study explores an advanced three-parameter generalized log-logistic (GLL) model by incorporating an innovative shape parameter into the conventional l...&quot;</p> <p><b>New page</b></p><div><br /> == Abstract ==<br /> <br /> &lt;p&gt;This study explores an advanced three-parameter generalized log-logistic (GLL) model by incorporating an innovative shape parameter into the conventional log-logistic framework, allowing for greater flexibility in modeling data with increasing, decreasing, and bathtub-shaped failure rates. The estimation challenge under adaptive progressively TypeII censored data is addressed through both classical (likelihood-based) and Bayesian inferential methods. Utilizing observed Fisher information, asymptotic confidence intervals for unknown parameters are derived, while a Markov chain in the Monte Carlo approach is employed to obtain Bayesian point estimates and the highest posterior density intervals. In Bayes&amp;rsquo; setup, the GLL parameters are presumed to have independent gamma conjugate priors against various symmetric and asymmetric losses. To examine the accuracy of the acquired estimators, an extensive Monte Carlo simulation is used. Four real-life data sets from the physical and biomedical industries are analyzed to illustrate the feasibility of the proposed techniques in an actual-world scenario. Numerical analyses revealed that the suggested model outperforms the other five models in the literature, including the alpha-power exponential, exponentiated exponential, log-logistic, Weibull, and gamma distributions. The findings emphasize the effectiveness of the Bayesian Markov chain Monte Carlo approach over frequentist techniques, reinforcing its practical significance in reliability analysis and survival studies.OPEN ACCESS Received: 27/03/2025 Accepted: 08/05/2025 Published: 15/08/2025&lt;/p&gt;<br /> <br /> <br /> <br /> == Document ==<br /> &lt;pdf&gt;Media:Draft_Sanchez Pinedo_618047731-3004-document.pdf&lt;/pdf&gt;</div>

JSanchez