كتابة النص: الأستاذ الدكتور يوسف أبو العدوس - جامعة جرش قراءة النص: الدكتور أحمد أبو دلو - جامعة اليرموك مونتاج وإخراج : الدكتور محمد أبوشقير، حمزة الناطور، علي ميّاس تصوير : الأستاذ أحمد الصمادي الإشراف العام: الأستاذ الدكتور يوسف أبو العدوس
فيديو بمناسبة الإسراء والمعراج - إحتفال كلية الشريعة بجامعة جرش 2019 - 1440
فيديو بمناسبة ذكرى المولد النبوي الشريف- مونتاج وإخراج الدكتور محمد أبوشقير- كلية تكنولوجيا المعلومات
التميز في مجالات التعليم والبحث العلمي، وخدمة المجتمع، والارتقاء لمصاف الجامعات المرموقة
محليا واقليميا وعالميا.
المساهمة في بناء مجتمع المعرفة وتطوره من خلال إيجاد بيئة جامعية، وشراكة مجتمعية محفزة للابداع،
وحرية الفكر والتعبير، ومواكبة التطورات التقنية في مجال التعليم، ومن ثم رفد المجتمع بما يحتاجه من
موارد بشرية مؤهلة وملائمة لاحتياجات سوق العمل.
تلتزم الجامعة بترسيخ القيم الجوهرية التالية:
الإلتزام الإجتماعي والأخلاقي، الإنتماء،العدالة والمساواة، الإبداع، الجودة والتميّز، الشفافية والمحاسبة، الحرية المنظبطة والمستقبلية.
Dr-Abdullah S. Al-jawarneh received his Ph.D. and M.Sc. in Statistics from the School of Mathematical Sciences, Universiti Sains Malaysia (USM), Penang, Malaysia. He completed his B.Sc. in Mathematics and minor in Mathematical statistics from Yarmouk University, Irbid, Jordan.
During his career, he taught several mathematics and statistics courses to the undergraduate students at Najran University, Saudi Arabia, from 2012 to 2019. He is now working as an Assistant Professor in the Department of Mathematics, Jerash University, Jerash, Jordan. He joined Jerash University in 2021. Dr-Abdullah has a good number of publications in journals having good Impact Factors. He specializes in regression analysis, time series data and signal process.
دكتور فى الاحصاء
مجال التخصص :الإحصاء التطبيقي
جامعة العلوم الماليزيا (USM)
The first part of the Hilbert–Huang transformation is named the empirical mode decomposition (EMD). Which employed to decompose the non-stationary and non-linear time series dataset into a finite set of orthogonal decomposition components. These components have been used in several studies as the new predictor variables to predict the behavior of the response variables. Adaptive LASSO (AdLASSO) regression is a technical penalized regression method used to determine the most relevant predictorson the response variable with achieving the consistency in terms of variable selection and ensuring that they are asymptotically normal. Hence, the main objective of this study is to apply the proposed EMD-AdLASSO method involving two cases of initial weights to identify the decomposedcomponents that exhibit the strongest effects to produce a consistent model and to improve the prediction accuracy. The simulation study and real dataset used the daily exchange rate dataset of three countries against the US dollar are applied. The results showed that the proposed method in the two cases of the initial weight outperformed other existing methods by effectively identifying the decomposition components, with high prediction accuracy. This is primarily observed in the case of using the ridge regression method based on the EMD as the initial weight in the proposed EMD-AdLASSO method.
This paper conducts empirical analysis to investigate the impact of domestic shocks relative to that of internal shocks on business cycle fluctuation in several developed Asian economies. The factors that determine the volume and impacts of these shocks on business cycle fluctuation are also in the scope of analysis. We apply a structural vector auto-regression model (SVAR) with Blancher and Quah’s identification. The results show that the domestic shocks are main sources of business cycle fluctuations in Asia countries; while the external shocks only have secondary impacts on the domestic economies. However, the impacts of external shocks are increasing over time. The factors that determine the volume of shocks on business cycle fluctuation include exchange rate, government consumption expenditure, terms of trade, trade openness and domestic monetary policy
The empirical mode decomposition (EMD) method is used to decompose the nonstationary and nonlinear signal into a finite set of orthogonal non-overlapping time scale components that include several intrinsic mode function components and one residual component. Elastic net (ELN) regression is a statistical penalized method used to address multicollinearity among predictor variables and identify the necessary variables that have the most effect on the response variable. This study proposed the use of the ELN method based on the EMD algorithm to identify the decomposition components of multivariate predictor variables with the most effect on the response variable under multicollinearity problems. The results of the numerical experiments and real data confirmed that the EMDELN method is highly capable of identifying the decomposition components with the presence or absence of multicollinearity among the components. The proposed method also achieved the best estimation and reached the optimal balance between the variance and bias. The EMD-ELN method also improved the accuracy of regression modelingcompared with the traditional regression models.
Elastic net (ELNET) regression is a hybrid statistical technique used for regularizing and selecting necessary predictor variables that have a strong effect on the response variable and deal with multicollinearity problem when it exists between the predictor variables. The empirical mode decomposition (EMD) algorithm is used to decompose the nonstationary and nonlinear dataset into a finite set of orthogonal intrinsic mode function components and one residual component. This study mainly aims to apply the proposed ELNET-EMD method to determine the effect of the decomposition components of multivariate time-series predictors on the response variable and tackle the multicollinearity between the decomposition components to enhance the prediction accuracy for building a fitting model. A numerical experiment and a real data application are applied. Results show that the proposed ELNET-EMD method outperforms other existing methods by capable of identifying the decomposition components that have the most significance on the response variable despite the high correlation between the decomposition components and by improving the prediction accuracy.
In this study, an elastic net (EN) regression model based on the empirical mode decomposition (EMD) algorithm is used in two applications, namely, numerical experiment and actual time series data. EMD is used to analyze a nonstationary and nonlinear signal dataset, which includes a set of orthogonal intrinsic mode functions (IMFs) and residual components. EN regression is used to select the most significant predictor variables influencing response variables and can address the multicollinearity problem between predictor variables. The main objective of this study is to apply the proposed method, EMD-EN, by using two variables for selecting important orthogonal IMFs and the residual components of predictor variables with significant effects on response variables. Moreover, this study uses the EMD-EN method in two different applications involving nonstationary and nonlinear problems. Results show that the proposed method outperforms other competitive methods in the numerical experiment and applications.
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