嘉宾简介：

练恒教授毕业于布朗大学，2007年获应用数学博士学位。他在新加坡南洋理工大学工作了几年，随后迁往悉尼的新南威尔士大学，现在香港城市大学担任副教授。 他的研究兴趣在于半参数估计，高维数据分析和函数型数据分析。

Abstract：

We consider smooth estimation of the conditional quantile process in linear models using penalized splines. For linear quantile regression problems, usually separate models are fitted at a finite number of quantile levels and then information from different quantiles is combined in interpreting the results. We propose a smoothing method based on penalized splines that computes the conditional quantiles all at the same time. We consider both fixed-knots and increasing-knots asymptotics of the estimator and show that it converges to a multivariate Gaussian process. Simulations show that smoothing can result in more accurate estimation of the conditional quantiles. The method is further illustrated on a real data set. Empirically (although not theoretically) we observe that the crossing quantile curves problem can often disappear using the smoothed estimator.