Regression diagnostics is the part of regression analysis whose objective is to investigate if the calculated model and the assumptions we made are consistent with the data. These …

Kernel Ridge Regression [140] is a regularized linear regression algorithm that uses a kernel trick to transform the data into a higher-dimensional space where a linear regression model can fit …

Logistic regression is an extension of “regular” linear regression. It is used when the dependent variable, Y, is categorical. We now introduce binary logistic regression, in which the Y variable …

In regression problems, the interval in the consequential proposition is sometimes reduced to a single point. (Matjaž Kukar, 2007) Logistic regression is a type of regression used for binary …

Jun 1, 2017 · Only regression of speech occurs and the child's functioning otherwise remains normal. Over several years, seizures generally subside (at the latest by ∼15 years) and their …

Sep 28, 2022 · In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss …

Dec 5, 2010 · The concept of multiple regression is similar to that of simpler regression, in that the dependent variable is related to the independent variables by a best fit. However, the …

Oct 1, 2024 · To refer to this process of regression in exercise, the term “regressions” is used in this text. To date, this concept has been little used in the literature and can be understood as a …

Feb 15, 2025 · RF regression is effective in exploring nonlinear relationships between variables (Gao et al., 2023; Wang et al., 2022). Therefore, this study also adopted RF regression to …

Regression analysis is another popular adjustment method. In regression analysis, all the measured baseline confounders are included as predictors to the outcome. This adjustment …