A Corrected Plug-in Method for Quantile Interval Construction Through a Transformed Regression
提出一种修正插入法，用于在异方差变换回归中构建原始响应变量条件分位数的置信区间，该方法计算简便，模拟和实证表现优于常用方法。
We propose a corrected plug-in method for constructing confidence intervals of the conditional quantiles of an original response variable through a transformed regression with heteroscedastic errors. The interval is easy to compute. Factors affecting the magnitude of the correction are examined analytically through the special case of Box–Cox regression. Monte Carlo simulations show that the new method works well in general and is superior over the commonly used delta method and the quantile regression method. An empirical application is presented.