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

linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...

_{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...

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

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...

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

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...

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

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