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...

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

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

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

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

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

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...

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

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

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

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

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

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

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

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...

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

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

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

Support vector regression (SVR) Decision tree Random forest k-nearest neighbors regression Kernel regression Principal component regression (PCR) Gaussian...

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

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

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

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

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

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

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