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

common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition...

Quantile Regression Averaging (QRA) is a forecast combination approach to the computation of prediction intervals. It involves applying quantile regression...

a sample in the same way. There is one fewer quantile than the number of groups created. Common quantiles have special names, such as quartiles (four groups)...

is the Tobit model, but quantile and nonparametric estimators have also been developed. These and other censored regression models are often confused...

the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability...

quality function for regression model is equivalent to doing weighted mean absolute error (MAE) regression, also known as quantile regression. This property...

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

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

Median regression may refer to: Quantile regression, a regression analysis used to estimate conditional quantiles such as the median Repeated median regression...

plot (quantile–quantile plot) is a probability plot, a graphical method for comparing two probability distributions by plotting their quantiles against...

Economics at UCL in 2018. Koenker is best known for his work on quantile regression and the regression analysis tool he developed is widely used across many disciplines...

commonly used with an alternative parameterization for performing quantile regression in a Bayesian inference context. Under this approach, the κ {\displaystyle...

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

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...

was later modified for regression. Unlike classification, which outputs p-values without a given significance level, regression requires a fixed significance...

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

may also be combined with LAD. Geometric median Quantile regression Regression analysis Linear regression model Absolute deviation Average absolute deviation...

outline is provided as an overview of and topical guide to regression analysis: Regression analysis – use of statistical techniques for learning about...

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

and maximum size in deep-sea turrid gastropods: an application of quantile regression". Marine Biology. 139 (4): 681–685. Bibcode:2001MarBi.139..681C....

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

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

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

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

Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes...

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

at George Washington University. Topics in her research include quantile regression and the application of biostatistics to cancer. Wang graduated from...

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

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