Understanding the effect of measurement error on quantile regressions
用小方差近似研究解释变量测量误差如何影响分位数回归函数，并给出近似误差的阶数，帮助研究者评估估计结果对测量误差的敏感性。
The impact of measurement error in explanatory variables on quantile regression functions is investigated using a small variance approximation. The approximation shows how the error contaminated and error free quantile regression functions are related. A key factor is the distribution of the error free explanatory variable. Exact calculations probe the accuracy of the approximation. The order of the approximation error is unchanged if the density of the error free explanatory variable is replaced by the density of the error contaminated explanatory variable which is easily estimated. It is then possible to use the approximation to investigate the sensitivity of estimates to varying amounts of measurement error.