AL-Qadisiyah Journal For Administrative and Economic sciences

Abstract study examines heart disease risk factors using SCAD-penalized quantile , to capture heterogeneous covariate effects across different of disease severity while achieving effective variable selection. Unlike mean- regression models, the approach allows regression coefficients to vary quantiles of the response , providing a detailed characterization of how predictors influence mild, , and severe forms of heart disease. The framework quantile regression with the smoothly clipped deviation penalty and is through a local linear approximation algorithm. analysis is conducted on real data obtained from a publicly available heart dataset. The empirical results pronounced distributional heterogeneity in severity and highlight clear -dependent patterns. Age and ST show consistently positive and effects toward higher quantiles, indiating stronger associations among patients severe disease, while maximum rate exhibits a stable protective effect across quantiles. Other predictors, incuding resting blood pressure, serum cholesterol, exercise-induced angina, mainly at upper quantiles, suggesting their primarily for severe . Overall, the findings demonstrate that SCAD- quantile regression provides a and interpretable framework for identifying meaningful heart disease factors and uncovering heterogeneity that is not using conventional methods.

Abstract This study aims to identify the most influential factors affecting the efficiency of digital financial services in Iraq within the context of financial inclusion. It employs the Reciprocal LASSO method as a modern statistical tool for variable selection. The analysis is based on annual data from 2010 to 2020, obtained from the Financial Access Survey (FAS) published by the International Monetary Fund. Nine financial inclusion indicators were used as explanatory variables. The model results revealed that variables related to the actual use of digital technologies such as the number of online or mobile banking users, the number of debit cards, and point-of-sale (POS) terminals have the greatest impact on service efficiency. In contrast, traditional infrastructure variables like the number of bank branches showed no significant effect.These findings highlight the importance of focusing on user behavior and the adoption of digital tools rather than relying solely on quantitative expansion of banking infrastructure. The study recommends enhancing digital financial literacy, improving digital infrastructure, and providing a flexible and secure regulatory environment to boost service efficiency and achieve effective financial inclusion.