Introduction of Anjuman Khuddām Al-Qur’ān
Anjuman Khuddām al-Qur’ān was established by Dr. Isrār Aḥmad in March 1972A. D. It was registered in November 1972A. D and Dr. Isrār Aḥmad was declared as lifetime president.[1]
The Memorandum of the Anjuman has the following contents:
Naḥmaduhū wa Nuṣallī ‘alā Rasūlehil karīm
Bismillāh al- Raḥmān al-Raḥīm
It is strongly felt that the dream of the renaissance of Islām and the second tenure for supremacy of righteous Dīn cannot be fulfilled without initiating a general movement to invoke faith in Muslim Ummah. To achieve this, it is mandatory that the source of faith and belief, i. e, the doctrine of intellect and wisdom by the Qur’ān should be publicized on a wide scale. Since we are in harmony with the thoughts of Dr. Isrār Aḥmad by overviewing his matchless task performed by him for the last four and half years, we, the few servants of The Divine Book hereby decide to set up “Central Anjuman Khuddām al-Qur’ān” which under the guidance of Dr. Isrār Aḥmad will keep striving the following objectives:
1. Learning and customization of the Arabic Language.
2. General persuasion and an invitation to study the Qur’ān.
3. Transmitting and publishing the Qur’ānic disciplines.
4. Adequate grooming and training of the youth who can make teaching and learning of the Qur’ān the life-mission, and
5. Setting up of aQur’ān Academy which may present across philosophy and wisdom of the Qur’ān at the highest academic level.
May Allāh enable us to achieve these objectives by putting in maximum effort and sacrifice! (Āmīn)
We are:...
هدفت الدراسة للوقوف على الوضع اللغوي في المملكة العربية السعودية والتعرف على القرارات الرسمية وخطاباتها التواصلية في تدبير مشكلات اللغة. ولتحقيق هدف الدراسة فقد استخدم الباحث المنهج الوصفي، وتوصل الباحث إلى مجموعة من النتائج أهمها أنَّ القرارات الخاصة باللغة العربية غطَّت جميع نواحي الحياة الخاصة بالمجتمع السعودي تقريبًا؛ وذلك لأنها اللغة الأم لهذه المنطقة ولأن تفشي غيرها من اللغات يؤثر سلبًا على هذه اللغة وعلى عادات وتقاليد أهل هذه البقعة الفاضلة من هذه الأرض.
This thesis deals with Bayesian inference of mixture densities using censored data. Type-I and type-II mixtures are considered that belong to two or one parameter exponential family. Selection of distribution is made keeping in view the novelty and applicability. These include Inverse Weibull, Pareto type-II, shifted exponential distribution and lastly mixture of Burr type XII and Rayleigh distributions that belong to type-II mixture model. These mixture distributions have not been explored so far in Bayesian setup. Bayes estimators for the parameters of the mixture models are derived in closed forms using type-I right censoring. To conduct Bayesian analysis, Informative (Gamma and Squared Raylegh) priors and non-informative (Uniform and Jeffreys) priors are considered while three loss functions, Squared Error Loss Function, Weighted loss function and Quadratic loss function are employed. A wide simulation study is made to scrutinize the properties of proposed Bayes estimators. Parameters of the mixture model are also tested through hypothesis testing procedure for inverse Weibull and Pareto type- II models. For the inverse Weibull mixture model when all parameters are unknown Bayes estimators can not be obtained in closed forms thus Gibbs sampling and Importance sampling techniques are used to obtain Bayes estimates in this case. Bayesian predictive density is used to obtain Bayes predictive intervals and reliability estimator. Predictive intervals for one and two sample prediction are also obtained that help to predict failure times of future observations. Bayes estimators using limiting form are also derived. Though type-I right censoring is considered throughout the dissertation, however, shifted exponential distribution is also explored through progressive censoring scheme. For the said case, Bayes estimators, credible intervals, Expected test termination time which is considered very useful for life testing experiments, are derived and evaluated. Applications of these mixtures are also presented by applying a real data set in each case.