In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form...

reduce them to a few principal components and then run the regression against them, a method called principal component regression. Dimensionality reduction...

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

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

regression and classification (e.g., functional linear regression). Scree plots and other methods can be used to determine the number of components included...

method Phosphocreatine, a phosphorylated creatine molecule Principal component regression, a statistical technique Protein/creatinine ratio, in urine...

Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...

Multilinear principal component analysis Multinomial distribution Multinomial logistic regression Multinomial logit – see Multinomial logistic regression Multinomial...

taken into account. It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models...

Tikhonov regularization. Tikhonov regularization, along with principal component regression and many other regularization schemes, fall under the umbrella...

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

applications of FPCA include the modes of variation and functional principal component regression. Functional linear models can be viewed as an extension of the...

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

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

calibration techniques such as partial-least squares regression, or principal component regression (and near countless other methods) are then used to...

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

Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which works...

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

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

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

Boosting (machine learning) Decision stump Chapman estimator Principal component regression Regularization (mathematics) Shrinkage estimation in the estimation...

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

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

factorization (NMF) Partial least squares regression (PLSR) Principal component analysis (PCA) Principal component regression (PCR) Projection pursuit Sammon mapping...

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

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

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

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

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...