Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...

explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression, which predicts multiple correlated dependent...

correlation among regressors is larger than a user-specified value. Just as ridge regression can be interpreted as linear regression for which the coefficients...

_{0}=c\mathbf {I} } is called ridge regression. A similar analysis can be performed for the general case of the multivariate regression and part of this provides...

process prior is known as Gaussian process regression, or kriging; extending Gaussian process regression to multiple target variables is known as cokriging...

least-angle regression algorithm. An important difference between lasso regression and Tikhonov regularization is that lasso regression forces more entries...

of the earliest uses of regularization is Tikhonov regularization (ridge regression), related to the method of least squares. In machine learning, a key...

is an advantage of Lasso over ridge regression, as driving parameters to zero deselects the features from the regression. Thus, Lasso automatically selects...

squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of...

called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which...

logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods...

especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent...

Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...

^{\mathsf {T}}\mathbf {y} .} Optimal instruments regression is an extension of classical IV regression to the situation where E[εi | zi] = 0. Total least...

(WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than...

Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional...

for example, the James–Stein estimator (which also drops linearity), ridge regression, or simply any degenerate estimator. The theorem was named after Carl...

principal components analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive filters and many others. Most...

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...

In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...

Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to...

surfaces in 3D Ridge (geometry), an (n-2)-dimensional element of a polytope Ridge regression, a statistical regularization method Ridge function, a multivariate...

Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness...

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...