النورسي ومعالجته النقدية الايجابية البناءة للقضايا

From the very first day, the scholars of the Ummah, Particularly from the time of Im฀m Sh฀f฀ movements of Islamic thought originated, which affected not only the Arabic world but the whole Islamic world. There had been movements of severe revenge and bloodshed and a lot of people were killed. Im฀m Nawras฀ is one of those unique people who served the Islamic thought from such dangerous storms. Day and night he made selfless efforts. He criticized the falsehood and injustice. The period of Im฀m Nawras฀ was plagued with severe gales of argumentations. This became the cause of Invitational, reformative and renewing movement of Im฀m Nawras฀. It faced the western and European attacks which appeared after Industrial and ideological revolutions of Europe. Before starting the movement, he did deep study of current affairs, Islamic thought and history. He studied the reasons due to which chaos of Islamic thought began. It was necessary to study all the situations and to fight with the contemporary Atheistic thought and wipe out its effects. So this article discusses intellectual contributions of Im฀m Nawras฀. He is great in handling the critical situation, and his conservative positive criticism is excellent. He is one of those luckiest persons who survived and got a chance to serve humanity. He was unique in handling intellectual issues away from dialectical demagoguery. Im฀m Nawras฀ really worked great for Islam. His principles regarding intellectual positive criticism, his philosophical thoughts, his criticism on mystic issues are presented here in this article. It is important to study and analyze Nawras฀ ’s amazing ability and his critical positive approach and treatment of constructive issues away from the ego.

Addressing Linear Regression Models With Correlated Regressors: Some Package Development in R

Regression analysis is widely used in manyelds such as economics,nance, technology and social sciences. A linear regression model describes the relationship between dependent variable and one or more regressors. A problem named multicollinearity is the existence/presence of nearly linear dependency among regressors. The existence of severe multicollinearity questions the accuracy of the parameters estimate in a linear regression model, because the variance of the ordinary least square estimator (OLSE) would be large enough. Therefore, detection of multicollinearity can be considered asrst step for giving solution of this problem. There are several indicators (diagnostic measures) for the quanti cation of collinearity among regressors available in the literature. Widely used and the most suggested diagnostics are values of pair-wise correlations among regressors, overall R2, VIF and tolerance limit, eigenvalues, condition number and condition indices etc. However, there is no unique method that can detect and measure the existence and strength of multicollinearity in data. For remedy or reduction of collinearity among regressors, biased estimators are suggested such as the ridge and Liu estimators in literature and are alternative to the OLSE. These alternative methods also improve the accuracy of the parameter estimates of linear regression models. The biased methods resemble each other and are based on similar principals. In this study, we propose two new detection methods for indication of collinearity vi existence and developed 3 packages in R language namely mctest, lmridge and liureg. The package mctest computes already existing collinearity diagnostic measures and our proposed measures. We also compare existing and our proposed collinearity diagnostics measures for the detection of collinearity existence among regressors. Our proposed collinearity diagnostics not only perform well for simulated but also for real collinear data sets. For computation and detection of existence of collinearity mctest package can be used. The package contains functions omcdiag for computation of overall and imcdiag for individual collinearity diagnostics as described in Chapter 3, Section 3.5. For estimation and testing of ridge coe cients, the ridge package lmridge can be used to compute ridge coe cients, di erent existing biasing parameters available in literature and testing of ridge coe cients with 15 ridge related statistics such as R2, adjusted-R2, mean square error and e ective degrees of freedom etc. Similarly, estimation and testing of Liu coe cient can be done using package liureg package. It computes biasing parameters from (Liu, 1993) and the Liu related statistics. In addition, numerical comparisons between existing estimator of ridge and Liu are also done by using existing collinear data set from Hald (1952), Longley (1967) and Malinvaud (1968).