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

intersection Line fitting Nonlinear least squares Regularized least squares Simple linear regression Partial least squares regression Linear function Weisstein...

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

method of least squares is a mathematical optimization technique that aims to determine the best fit function by minimizing the sum of the squares of the...

regression, (ii) threshold regression, (iii) smooth regression, (iv) logistic link regression, (v) Box–Cox transformed regressors ( m ( x , θ i ) = θ 1 +...

In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational...

statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed...

(as with least absolute deviations regression), or by minimizing a penalized version of the least squares cost function as in ridge regression (L2-norm...

Estimation theory Partial least squares path modeling Partial least squares regression Principal component analysis Regression analysis Regression validation...

The partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS-SEM) is a method for structural equation modeling...

variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions...

In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there...

corresponds to the regression coefficient for Xi of a regression of Y on all of the covariates. The residuals from the least squares linear fit to this...

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

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

is still unaccounted for. For regression models, the regression sum of squares, also called the explained sum of squares, is defined as S S reg = ∑ i (...

combines much of the simplicity of linear least squares regression with the flexibility of nonlinear regression. It does this by fitting simple models to...

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

packages perform least squares regression analysis and inference. Simple linear regression and multiple regression using least squares can be done in some...

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

Latent variable Least squares Moving average Multivariate analysis Multivariate statistics Observational study Partial least squares regression Stationary...

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

Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting...

squares Partial least squares regression Partial leverage Partial regression plot Partial residual plot Particle filter Partition of sums of squares Parzen...

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

used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the...

Though the idea of least absolute deviations regression is just as straightforward as that of least squares regression, the least absolute deviations...

Chemometrics 1(4):201-219 doi:10.1002/cem.1180010403 Abdi, H. (2003) Partial least squares regression In Lewis-Beck, M., Bryman, A., Futing, T. (Eds.). Encyclopedia...

lithium mining company. Palomar–Leiden survey of minor planets Partial least squares regression, a statistical method Plasma spectrometer, an instrument aboard...

Latent variable model Item response theory Partial least squares path modeling Partial least squares regression Proxy (statistics) Rasch model Structural...