Explore: Régression Quantile

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

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

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)

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

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

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

function (Friedman's SuperSmoother) in R Quantile LOESS – A method to perform Local regression on a 'Quantile moving window (with R code) Nate Silver,

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

{\bigg ]}}   at the 1 − α confidence level, where q denotes the quantile function of standard normal distribution, and [·]jj is the j-th diagonal