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

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

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

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...

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

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

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

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...

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

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

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

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

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

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

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

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

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

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

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

simple function (also called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial...

data analysis OLS Partial least squares regression Pattern recognition Principal component analysis (PCA) Regression analysis Soft independent modelling of...

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

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