We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associat...

Abstract The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ET Ref ) in different climates Iran based on Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), severe (D). For this purpose, climatic data 33 mete...

Abstract. Commercially available anisotropic magnetoresistive (AMR) magnetometers exhibit on the order of 1 nanotesla (nT) sensitivity in small size, weight, and power (SWaP) packages. However, AMR magnetometer accuracy is diminished by properties such as static offsets, gain uncertainty, off-axis coupling, temperature effects. This work presents a measurement magnitude these effects for Honeyw...

Operating solar photovoltaic (PV) panels at the maximum power point (MPP) is considered to enrich energy conversion efficiency. Each MPP tracking technique (MPPT) has its efficiency and methodology for MPP. This paper introduces a new method operating PV panel by implementing multivariate linear regression (MLR) machine learning algorithm. The MLR model in this study trained tested using data c...

Heat treatment is one of the essential technologies in industrial manufacturing, and carburization processes heat-treatment technology. With increase technological requirements production, optimization carburizing technology desired. However, most mechanism models are derived from material properties related physical principles, which involved huge amounts parameters. Some parameters (such as d...