Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to...

models (e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely...

In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations...

non-linear relation between a pair of random variables X and Y. In any nonparametric regression, the conditional expectation of a variable Y {\displaystyle Y}...

method to estimate a probability distribution. Nonparametric regression and semiparametric regression methods have been developed based on kernels, splines...

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

Linear regression Simple linear regression Logistic regression Nonlinear regression Nonparametric regression Robust regression Stepwise regression Regression...

quantile and nonparametric estimators have also been developed. These and other censored regression models are often confused with truncated regression models...

Linear equation Logistic regression M-estimator Multivariate adaptive regression spline Nonlinear regression Nonparametric regression Normal equations Projection...

data (e.g. using ordinary least squares). Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions...

represents an improved technique in the neural networks based on the nonparametric regression. The idea is that every training sample will represent a mean to...

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

"multilevel regression" and "poststratification" ideas of MRP can be generalized. Multilevel regression can be replaced by nonparametric regression or regularized...

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

doi:10.1007/978-0-387-84858-7, [1] (eq.(5.16)) Fox, J. (2000). Nonparametric Simple Regression: Smoothing Scatterplots. Quantitative Applications in the Social...

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

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

adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique...

In statistics, an additive model (AM) is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981) and is an...

classification, regression analysis, and cluster analysis on data in an implicit space. This usage is particularly common in machine learning. In nonparametric statistics...

Passing–Bablok regression is a method from robust statistics for nonparametric regression analysis suitable for method comparison studies introduced by...

models Nonparametric regression Isotonic regression Semiparametric regression Local regression Total least squares regression Deming regression Errors-in-variables...

for his work on data visualization, particularly on nonparametric regression and local regression. He is remembered as one of the developers of the S...

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

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

Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...

Boor's official site [1]. Green, P. J.; Silverman, B.W. (1994). Nonparametric Regression and Generalized Linear Models: A roughness penalty approach. Chapman...

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

case, the "regression" effect is statistically likely to occur, but in the second case, it may occur less strongly or not at all. Regression toward the...

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...