NONSTANDARD QUANTILE-REGRESSION INFERENCE
探讨在分位数回归中，通过使用对条件密度不一致的尺度统计量来避免估计条件密度，从而得到非标准但便于重抽样近似的极限分布。
It is well known that conventional Wald-type inference in the context of quantile regression is complicated by the need to construct estimates of the conditional densities of the response variables at the quantile of interest. This note explores the possibility of circumventing the need to construct conditional density estimates in this context with scale statistics that are explicitly inconsistent for the underlying conditional densities. This method of studentization leads conventional test statistics to have limiting distributions that are nonstandard but have the convenient feature of depending explicitly on the user’s choice of smoothing parameter. These limiting distributions depend on the distribution of the conditioning variables but can be straightforwardly approximated by resampling.