In statistics, regression validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables...

Look up validation or validate in Wiktionary, the free dictionary. Validation may refer to: Data validation, in computer science, ensuring that data inserted...

Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how...

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

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

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

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

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

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

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

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

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

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

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

Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information...

Density Based Empirical Likelihood Ratio tests In regression analysis, more specifically regression validation, the following topics relate to goodness of fit:...

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

In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where...

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

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

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

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 consistent...

In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...

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

In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations...

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

uses the sample median; to estimate the population regression line, it uses the sample regression line. It may also be used for constructing hypothesis...

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...

In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output...