In statistics, Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted...
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general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In...
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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...
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involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate statistics...
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explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression, which predicts multiple correlated dependent...
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including Bayesian regression and least squares fitting to variance stabilized responses, have been developed. Ordinary linear regression predicts the...
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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...
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Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...
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non-linear models (e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis...
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linear model for non-normal distributions Bayesian linear regression, where statistical analysis is from a Bayesian viewpoint Bayesian multivariate linear...
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distributed if every linear combination of its k components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit...
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In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...
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Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...
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an event as a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the...
127 KB (20,643 words) - 21:34, 15 October 2024
Quantile regression is an extension of linear regression used when the conditions of linear regression are not met. One advantage of quantile regression relative...
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sampling Bayesian information criterion Bayesian linear regression Bayesian model comparison – see Bayes factor Bayesian multivariate linear regression Bayesian...
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List of things named after Thomas Bayes (redirect from Bayesian)
of redirect targets Bayesian multivariate linear regression – Bayesian approach to multivariate linear regression Bayesian Nash equilibrium – Game theory...
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In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...
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Multilevel model (redirect from Hierarchical regression)
seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became...
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Gaussian process (redirect from Bayesian Kernel Ridge Regression)
t_{k}}=(X_{t_{1}},\ldots ,X_{t_{k}})} is a multivariate Gaussian random variable. That is the same as saying every linear combination of ( X t 1 , … , X t k )...
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{\displaystyle m(x)} is some deterministic function. Linear regression is a restricted case of nonparametric regression where m ( x ) {\displaystyle m(x)} is assumed...
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Segmented linear regression is segmented regression whereby the relations in the intervals are obtained by linear regression. Segmented linear regression with...
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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...
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In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...
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In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where...
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Design matrix (redirect from Data matrix (multivariate statistics))
vector of ones. This section gives an example of simple linear regression—that is, regression with only a single explanatory variable—with seven observations...
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Ordinary least squares (redirect from Ordinary least squares regression)
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...
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Empirical Bayes method (redirect from Empirical Bayesian)
model, as well specific models for Bayesian linear regression (see below) and Bayesian multivariate linear regression. More advanced approaches include...
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Time series (redirect from Multivariate time series)
Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting. Oxford University Press. ISBN 978-0-19-803834-4.[page needed] Regression Analysis...
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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...
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