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

physiology), a type of muscle contraction Isotonic regression, a type of numerical analysis Isotonic, one of three types of tonicity that characterize...

(statistics) Local regression Non-parametric statistics Semiparametric regression Isotonic regression Multivariate adaptive regression splines Cherkassky...

regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression...

1. {\textstyle p=1.} Non-metric scaling is defined by the use of isotonic regression to nonparametrically estimate a transformation of the dissimilarities...

multilevel regression with poststratification model involves the following pair of steps: MRP step 1 (multilevel regression): The multilevel regression model...

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

called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which...

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

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

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

Platt scaling, which learns a logistic regression model on the scores. An alternative method using isotonic regression is generally superior to Platt's method...

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

ordinary isotonic regression in most cases, and also offering the first viable interval estimator for isotonic regression in general. Isotonic regression estimators...

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...

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

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

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

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

In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output...

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

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

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

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

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

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

logistic regression, multilayer perceptrons, and random forests. An alternative approach to probability calibration is to fit an isotonic regression model...

In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where...

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...

approach, see Bennett (2002) Isotonic regression, see Zadrozny and Elkan (2002) Platt scaling (a form of logistic regression), see Lewis and Gale (1994)...