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...

involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate statistics...

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

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

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...

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

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

distributed if every linear combination of its k components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit...

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

function. Linear regression is a restricted case of nonparametric regression where m ( x ) {\displaystyle m(x)} is assumed to be a linear function of...

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

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

variables to higher-dimensional space. Multivariate linear regression extends the concept of linear regression to handle multiple dependent variables...

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

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

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

distribution over functions in Bayesian inference. Given any set of N points in the desired domain of your functions, take a multivariate Gaussian whose covariance...

Segmented linear regression is segmented regression whereby the relations in the intervals are obtained by linear regression. Segmented linear regression with...

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

categorical dependent variable (i.e. the class label). Logistic regression and probit regression are more similar to LDA than ANOVA is, as they also explain...

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...

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

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

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

variables whose linear combination follows a multivariate normal distribution, multivariate variance-covariance matrix homogeneity, and linear relationship...

Rogers-Stewart. One application of normality tests is to the residuals from a linear regression model. If they are not normally distributed, the residuals should...