Introduction We aimed to determine the association between paternal labour migration and the growth of the left-behind ...

In today’s fast-paced, ever-evolving world, the complexity of challenges faced in both business and everyday life requires a shift in how we approach problem solving. Traditional linear thinking, ...

What is linear regression in machine learning ? Understanding Linear Regression in machine learning is considered as the basis or foundation in machine learning. In this video, we will learn what is ...

a Sharpe, EVaR-Sharpe, and LVaR-Sharpe represent the traditional Sharpe ratio, Sharpe ratio modified based on expectile, and Lp quantile, respectively. [12] Yao, H. (2011) Linear Heteroscedastic ...

Multiple times while sifting through the rubble of the latest Bengals defensive debacle, coordinator Lou Anarumo repeatedly made a roller-coaster motion with his arm. He used it to describe individual ...

Abstract: The accurate forecasting of nonlinear regression problems is essential in numerous real-world applications, and enhancing the precision of predictions in such problems is significantly ...

Department of Chemical Engineering, University of Louisiana, Lafayette, Louisiana 70504, United States Energy Institute of Louisiana, University of Louisiana ...

Expansive soils, known for their significant volume change with variations in moisture content, are widely distributed around the globe. Due to their swelling properties, expansive soils pose ...

During the installation of a cable oil terminal, it is easy to leave scratches on the main insulation owing to uneven forces when removing the semi-conductive layer. Scratch defects cause field ...

Nonlinear regression models represent an alternative way to describe plant growth. In this study, we aimed to model the growth of linseed using four methods for data collection (longitudinal, mean, ...