Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

SVMs applied to a regression problem perform a linear regression in the feature space using the -insensitive loss as cost function and, at the same time, regularizing the weights

To examine the cross-sectional association of PF and BC variables with MetS scores, we used linear regression mod- els, where PF variables (i.e., 20-m shuttle run test, relative VO

show that the proposed models can outperform classical - SVR models over two real-world regression tasks, and also that problem-independent techniques applied to tackle the problem

The focus of this paper was to investigate the contribution of different physicochemical parameters to bulking episodes, and to develop logistic regression models to predict

In all cases, measures of thickness (average thickness in the case of random forest and log 10 of maximum thickness in the case of ANN and multiple linear regression) are

Then, § 3.2 deals with the linear and nonlinear size structured models for cell division presented in [13] and in [20, Chapter 4], as well as with the size structured model

Secondly, from an explanatory point of view and through logistic regression models, we aimed to estimate which structural or independent variables best explain the changes in the

Therefore, this study evaluated the predictive performance of regression models (Simple Linear Regres- sion [SLR], Multiple Linear Regression [MLR] and Generalized Additive