Search results

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

Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes...

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...

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

The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals (a residual...

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

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

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

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

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

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

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

analysis. Ridge regression was developed as a possible solution to the imprecision of least square estimators when linear regression models have some...

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 (...

is the hat matrix, or the projection matrix in linear regression. The least-squares regression line is given by y = a x + b {\displaystyle y=ax+b} , where...

linear regression in that it accounts for errors in observations on both the x- and the y- axis. It is a special case of total least squares, which allows...

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

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

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

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