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

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

of the regressors can be a non-linear function of another regressor or of the data values, as in polynomial regression and segmented regression. The model...

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

prominent member in the family of functional polynomial regression models is the quadratic functional regression given as follows, E ( Y | X ) = α + ∫ 0 1...

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

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

the context of regression analysis, such combinations are known as interaction features. The (implicit) feature space of a polynomial kernel is equivalent...

and profile similarity indices can be avoided by using polynomial regression. Polynomial regression involves using measures of the person and environment...

Savitzky–Golay filter Kernel methods Kernel density estimation Local regression Kernel regression Li, Q. and J.S. Racine. Nonparametric Econometrics: Theory and...

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

methodology Optimal designs Plackett–Burman design Polynomial and rational function modeling Polynomial regression Probabilistic design Surrogate model Bayesian...

In numerical analysis, polynomial interpolation is the interpolation of a given data set by the polynomial of lowest possible degree that passes through...

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

overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline...

Nonparametric regression is a form of regression analysis where the predictor does not take a predetermined form but is completely constructed using information...

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...

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

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

_{3}x^{2}} . Cubic, quartic and higher polynomials. For regression with high-order polynomials, the use of orthogonal polynomials is recommended. Numerical smoothing...

parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form:...

regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression,...

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...

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 statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...

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

maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers...

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