Quantile Regression

Quantile regression models the relationship between a set of predictor (independent) variables and specific percentiles (or "quantiles") of a target (dependent) variable, most often the median. It has two main advantages over Ordinary Least Squares regression:

• Quantile regression makes no assumptions about the distribution of the target variable.
• Quantile regression tends to resist the influence of outlying observations