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...

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...

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

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

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

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

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

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

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

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

general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In...

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

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

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

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

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

an event as a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the...

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

Microsoft Excel), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage...

seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became...

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

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...

model, as well specific models for Bayesian linear regression (see below) and Bayesian multivariate linear regression. More advanced approaches include...

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

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...

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