Proposing Robust LAD-Atan Penalty of Regression Model Estimation for High Dimensional Data

       لاقت قضية نموذج الانحدار اهتمامًا بالغ الأهمية لاختيار المتغيرات، إذ انه يؤدي دورًا أساسيًا في التعامل مع البيانات ذات  الابعاد العالية. يتم استخدام معكوس الظل الذي يشير إليه  دالة جزاء Atan في كل من التقدير والاختيار المتغير كطريقة فعالة. ومع ذلك ، فإن دالة الجزاء  Atan حساسة جدًا للقيم الشاذة لمتغيرات الاستجابة أو توزيع ملتوي للأخطاء أو توزيع ذو ذيل ثقيل. بينما  : LAD هي وسيلة جيدة للحصول على حصانة تقدير الانحدار. ان الهدف الاساس من هذا البحث هو اقتراح مُقدّر Atan يجمع بين هاتين الفكرتين في آن واحد. لقد اظهرت تجارب المحاكاة وتطبيق البيانات الحقيقية أن مقدّر LAD-Atan المقترح هو الافضل مقارنة بالمقدرات الاخرى.           The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator has superior performance compared with other estimators. &nbsp

Kernel estimation of returns of retirement funds of employers based on monetary earnings (subscriptions and compensation) via regression discontinuity in Iraq

Regression Discontinuity (RD) means a study that exposes a definite group to the effect of a treatment. The uniqueness of this design lies in classifying the study population into two groups based on a specific threshold limit or regression point, and this point is determined in advance according to the terms of the study and its requirements. Thus , thinking was focused on finding a solution to the issue of workers retirement and trying to propose a scenario to attract the idea of granting an end-of-service reward to fill the gap ( discontinuity point) if it had not been granted. The regression discontinuity method has been used to study and to estimate the effect of the end -service reward on the cutoff of insured workers as well as the increase in revenues resulting from that. The research has showed that this reward has a clear effect on increasing revenues due to the regularity of workers in their work and their work continuity . It has also found that using Local Linear Smother (LLS) by using three models of bandwidth selection. Its results after the analysis in the Regression program have been as follows: The CCT (Calonico, Cattaneo & Titiunik) beamwidth gives the best performance followed by the local linear regression using the LK (Lembens and kalyanman) beamwidth. The real data has been used in sample size 71 represented in compensation as a variable of effectiveness (illustrative) X and the revenue as a result or an approved variable Y, while the results of the traditional OLS estimation method have not been good enough