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 proposes a Predictive Expectile Regression model with SCAD penalty to improve forecasting accuracy and variable selection in high-dimensional financial data. By integrating the SCAD regularization into expectile regression, the model effectively captures tail risk and manages multicollinearity in non-Gaussian environments. Simulation experiments under normal, skewed, and heavy-tailed errors show that the proposed ER–SCAD consistently achieves the lowest MSE and MAED, confirming its robustness and sparsity compared with ER, ER–LASSO, and ER–EN. Application to S&P 500 daily data (2015–2024) further demonstrates superior predictive performance and numerical stability. The findings indicate that combining expectile regression with SCAD penalty provides an efficient and interpretable framework for high-dimensional financial prediction and risk assessment.