Quantile Regression
分位数回归是经典最小二乘法的扩展，用于估计条件分位数函数，通过最小化加权绝对误差和来估计不同分位点，适用于CEO薪酬、食品支出和婴儿出生体重等模型。
Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors. Other conditional quantile functions are estimated by minimizing an asymmetrically weighted sum of absolute errors. Quantile regression methods are illustrated with applications to models for CEO pay, food expenditure, and infant birthweight.