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
Quantile regression is widely used for researching in industries such as ecology, healthcare, and financial economics.