Ibn Taymīyah, Aḥmad ibn ʻAbd al-Ḥalīm, 1263-1328. Naqḍ al-manṭiq; Islamic philosophy; logic.

Islamic philosophy; Hanbalites; doctrines; creation (Islam); early works to 1800.

al-juzʼ 1. [Without special title] -- al-juzʼ 2. al-Masmūḥ wa-al-mamnūʻ fī firāsh al-zawjīyah.

Introduction : La philosophie en Égypte -- La philosophie chretienne arabe contemporaine : Kanawati et Youssef Karam -- Le discours éthique en Égypte -- Naguib Mahfouz et la philosophie -- La philosophie et la revolution en Égypte.

When the first r lower extreme order statistics of a sample of large size n, 1 < r < s < n, are observed, asymptotic predictive intervals of the future extreme order statistic with a rank s are constructed. The only assumption that we adopt is that the first failure time is attracted to the Weibull distribution. In addition, we suggest an efficient point estimator of its shape parameter and then a confidence interval is constructed for it. Moreover, new interesting (...) asymptotic properties of the distributions that belong to the minimum domain of attraction of the Weibull distribution are revealed. Furthermore, extensive simulation studies are conducted to demonstrate the efficiency of the proposed methods. Two real datasets are analyzed to illustrate and corroborate the obtained results. The main results concern large samples. (shrink)

When the first r lower extreme order statistics of a sample of large size n, 1 < r < s < n, are observed, asymptotic predictive intervals of the future extreme order statistic with a rank s are constructed. The only assumption that we adopt is that the first failure time is attracted to the Weibull distribution. In addition, we suggest an efficient point estimator of its shape parameter and then a confidence interval is constructed for it. Moreover, new interesting (...) asymptotic properties of the distributions that belong to the minimum domain of attraction of the Weibull distribution are revealed. Furthermore, extensive simulation studies are conducted to demonstrate the efficiency of the proposed methods. Two real datasets are analyzed to illustrate and corroborate the obtained results. The main results concern large samples. (shrink)