In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

Abstract

Objective of this research focused on testing the impact of internal corporate governance instruments in the management of working capital and the reflection of each of them on the Firm performance. For this purpose, four main hypotheses was formulated, the first, pointed out its results to a significant effect for each of corporate major shareholders ownership and Board of Directors size on the net working capital and their association with a positive relation.  The second, explained a significant effect of net working capital on the economic value added, and their link inverse relationship, while the third, explored a significant effect for each of the corporate major shareholders ownershi