Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

In statistics, Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted...

Generalised linear model for non-normal distributions Bayesian linear regression, where statistical analysis is from a Bayesian viewpoint Bayesian multivariate...

Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients...

term. Bayesian linear regression applies the framework of Bayesian statistics to linear regression. (See also Bayesian multivariate linear regression.) In...

including Bayesian regression and least squares fitting to variance stabilized responses, have been developed. Ordinary linear regression predicts the...

Quantile regression is an extension of linear regression used when the conditions of linear regression are not met. One advantage of quantile regression relative...

non-linear models (e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis...

{\displaystyle {\boldsymbol {\beta }}} and is therefore equivalent to Bayesian linear regression. Regularized least squares: the elements of β {\displaystyle {\boldsymbol...

descriptions of redirect targets Bayesian multivariate linear regression – Bayesian approach to multivariate linear regression Bayesian Nash equilibrium – Game...

sampling Bayesian information criterion Bayesian linear regression Bayesian model comparison – see Bayes factor Bayesian multivariate linear regression Bayesian...

adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique...

include regression-based approaches, such as stacked-regression. A more general class of regression-based multi-fidelity methods are Bayesian approaches...

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

estimators when linear regression models have some multicollinear (highly correlated) independent variables—by creating a ridge regression estimator (RR)...

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

In statistics, the Bayesian information criterion (BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among...

the probit regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x ,...

MANCOVA, ordinary linear regression, t-test and F-test. The general linear model is a generalization of multiple linear regression to the case of more...

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

Anti-spam techniques Bayes classifier Bayesian network Bayesian poisoning Email filtering Linear classifier Logistic regression Markovian discrimination Mozilla...

General linear model Ordinary least squares Generalized least squares Simple linear regression Trend estimation Ridge regression Polynomial regression Segmented...

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

used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the...

{\displaystyle {\boldsymbol {\beta }}} is given in the article on Bayesian linear regression, although specified with different notation, while the conditional...

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

(2018) shows, several shrinkage estimators – such as Bayesian linear regression, ridge regression, and the (adaptive) lasso – make use of this decomposition...

machine learning, ordinal regression may also be called ranking learning. Ordinal regression can be performed using a generalized linear model (GLM) that fits...

(SOM) Logistic regression Ordinary least squares regression (OLSR) Linear regression Stepwise regression Multivariate adaptive regression splines (MARS)...

outputting a single value, as in linear regression. Binary regression is usually analyzed as a special case of binomial regression, with a single outcome ( n...