In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron...

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

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

regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression...

statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed...

linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares...

linear regression models. This simple case reveals a substantial amount about the estimator. These include its relationship to ridge regression and best...

such as the least angle regression algorithm. One of the prime differences between Lasso and ridge regression is that in ridge regression, as the penalty...

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

generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation...

regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x , θ i ) = θ 1 +...

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

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

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic...

<1} , one obtains quantile regression. The case of τ = 1 / 2 {\displaystyle \tau =1/2} gives the standard regression by least absolute deviations and is...

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

In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there...

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

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

Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models...

function Partial correlation Partial least squares Partial least squares regression Partial leverage Partial regression plot Partial residual plot Particle...

Stars), a rap group Launch and recovery system (diving) Least-angle regression, a regression algorithm for high-dimensional data Lesotho Amateur Radio...

distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead...

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

(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the...

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

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable...

Analysis (KPCA) K-Means Clustering Least-Angle Regression (LARS/LASSO) Linear Regression Bayesian Linear Regression Local Coordinate Coding Locality-Sensitive...

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

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