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 …

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 …

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 …

Apr 1, 2025 · The proposed GGTWR framework encompasses various generalized linear models, e.g. Poisson regression, negative binomial regression, and other models of the exponential …

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 …

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 …

May 1, 2022 · Note that the Cox regression model assumes a log-linear and additive fun-ction in terms of the covariates, as well as the between-group proportional hazards. In fact, there are a …

Aug 1, 2021 · Regression (and other) models often include binary indicator (dummy) variables. While such variables can be included when calculating Mahalanobis distances and leverages, …

Dec 10, 2013 · Regression models with one dependent variable and more than one independent variables are called multilinear regression. In this study, data for multilinear regression …