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

model or proportional odds logistic regression is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered...

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

: 189  In regression analysis, outcomes (dependent variables) that are ordinal variables can be predicted using a variant of ordinal regression, such as...

also used as a quality measure of binary choice or ordinal regression (e.g., logistic regressions) and credit scoring models. We say that two pairs (...

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

logistic regression generalized to multinomial logistic regression. If the multiple categories are ordered, one can use the ordinal logistic regression (for...

training data has a numerical or ordinal score. Then the learning-to-rank problem can be approximated by a regression problem — given a single query-document...

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

measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful...

best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio. This framework of distinguishing levels of measurement...

independent variables. Multivariate logistic regression uses a formula similar to univariate logistic regression, but with multiple independent variables...

(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the...

linear regression models. This simple case reveals a substantial amount about the estimator. These include its relationship to ridge regression and best...

regression does. Linear regression assumes homoscedasticity, that the error variance is the same for all values of the criterion. Logistic regression...

machine learning) and further Cramer distance performed better in ordinal regression than KL distance or the Wasserstein metric. While CRPS is widely used...

Regression diagnostic Regression dilution Regression discontinuity design Regression estimation Regression fallacy Regression-kriging Regression model validation...

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

Saleh, Abdelrhman; Glass, James; Nakov, Preslav (2019). "Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political...

Bradley–Terry model and logistic regression. Both employ essentially the same model but in different ways. In logistic regression one typically knows the parameters...

cereal, then probit or logit regression (or multinomial probit or multinomial logit) can be used. If both variables are ordinal, meaning they are ranked in...

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

developments, including Poisson regression, ordinal logistic regression, quantile regression and multinomial logistic regression that described by Fallah in...

simple function (also called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial...

the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function...

it is considered a tie. By reducing detailed measures to a sequence of ordinal numbers, rankings make it possible to evaluate complex information according...

distribution (the Bernoulli distribution) and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable"...

logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.)...

ordinal versions of the above-mentioned ACC, PACC, and SLD methods. Methods for the regression case include Regress and splice and Adjusted regress and...

categorical variable. Categorical variables have two types of scales, ordinal and nominal. The first type of categorical scale is dependent on natural...