Principal Component Analysis

The Persian translation of the emotion regulation strategies inventory (Gross & John, 2003) was validated among Iranian EFL teachers. The predictive power of variables, i.e. educational background, working experience, gender, and age was also appraised. To do so, 250 EFL teachers with at least five-year teaching experience at the universities of two states, Isfahan and Fars, were invited to take part in the study. The non-random convenient sampling technique was then adopted. Filling out the inventory was done after the class time. The results of the principal component analysis (varimax rotation) verified the original two-factor model. The multiple regression analysis done by AMOS software also revealed that demographic variables could significantly affect teachers’ emotion regulation, though their effect in the present sample was small (R2=0.08 and R2=0.02). The results also suggested that the teachers disagreed about the use of expressive suppression in their classes (m=3.28) and were rather undecided as to the use of cognitive reappraisal in their teaching (m=4.49).

In this paper we construct a modeling for detection of banks which are experiencing serious problems. Sample and variable set of the study contains 30 banks of Iran during 2006-2014 and their financial ratios. Well known multivariate statistical technique (principal component analysis) was used to explore the basic financial characteristics of the banks, and discriminant Logit and Probit models were estimated based on these characteristics. Results suggest that the model can be used as an analytical decision support tool in both on-site and off-site bank monitoring system to detect the banks which are experiencing serious problems. JEL Classifications: C49, G21, G33

The present study aims at developing an aggregate financial stability index by using banking sector indices to assess financial stability and examine if the variable of credit-to-GDP gap corresponds to its long-term trend which represents the macro-prudential indicator has co-movement with the built financial stability index? To this end, monthly banking balance sheet data were collected from the Central Bank of the Islamic Republic of Iran from March 2007 to March 2017. Co-movement of two time series was assessed at two dimensions of time and frequency through wavelet analysis. It can be observed that there is a greater relationship between the two variables at short-term and medium-term. In the short-term, there is a negative correlation between financial stability and the representative of the macro-prudential variable. The increase of the credit-to-GDP gap results in a decrease in financial stability while these variables are positively correlated at medium-term. An increase in the credit-to-GDP gap increases financial stability, whereas such a relationship cannot be observed for the long-term. Thus, it seems necessary to adopt a macro-prudential policy more at medium term.

The evolution of today's application technologies requires a certain level of robustness, reliability and ease of integration. We choose the Fields Programmable Gate Array (FPGA) hardware description language to implement the facial recognition algorithm based on "Eigen faces" using Principal Component Analysis. In this paper, we first present an overview of the PCA used for facial recognition, then use a VHSIC Hardware Description Language (VHDL) simulation and design platform, which is the ISE. We describe the operation of each block and implement, thereafter, the computation of the global centered images. This corresponds to the first step of the PCA algorithm to assess its performance. The comparison of the results of this implementation with that of MATLAB confirmed the operability and effectiveness of this method for centralizing images. We also implemented the last part of this algorithm which is the computation of the Manhattan distance. The tests have given very satisfactory results.