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

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}...

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

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

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

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...

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

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

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...

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...

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

Least absolute deviations Model selection Nonparametric regression Tikhonov regularization "What is lasso regression?". ibm.com. 18 January 2024. Retrieved...

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...

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...

statistics, Alternating Conditional Expectations (ACE) is a nonparametric algorithm used in regression analysis to find the optimal transformations for both...

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

from the conventional linear model. Developments towards fully nonparametric regression models for functional data encounter problems such as curse of...

specified parametric form (for example a polynomial, or an un-penalized regression spline of a variable) or may be specified non-parametrically, or semi-parametrically...

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

linear statistics Gaussian process Multivariate interpolation Nonparametric regression Radial basis function interpolation Space mapping Spatial dependence...

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

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

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

nearest neighbor. The k-NN algorithm can also be generalized for regression. In k-NN regression, also known as nearest neighbor smoothing, the output is the...

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

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...

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