Quantile regression

Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional mean of the response variable across values of the predictor variables, quantile regression estimates the conditional median (or other quantiles) of the response variable. There is also a method for predicting the conditional geometric mean of the response variable,. Quantile regression is an extension of linear regression used when the conditions of linear regression are not met. It was introduced by Roger Koenker in 1978. As a complementary and extended approach to the least squares method, quantile regression addresses the limitations of least squares method in the presence of heteroscedasticity and ensures the robustness of quantile regression through its robustness to outliers, which compensates for the weakness of least squares method in dealing with outlier data.