报告题目：How Asymptotics Meets Application：Better Nonparametric Confidence Intervals for Quantile Regression

报 告 人：郭绍俊  中国人民大学 副教授

报告时间：2020年6月24日 下午 13:30-14:30

报告地点：腾讯会议

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会议 ID：253 244 489

会议密码：0624

校内联系人：赵世舜 zhaoss@jlu.edu.cn

报告摘要：

In this article we revisit the classical problem of how to construct valid nonparametric confidence intervals for the conditional quantile function. We first propose an adaptive bias correction procedure based on local polynomial smoothing to estimate the conditional quantile. To account for the effect of the estimated bias, we consider a new asymptotic framework that the ratio of the bandwidth to the pilot bandwidth tends to some positive constant rather than zero as the sample size grows, under which we establish an alternative asymptotic normality of the proposed estimator. An interesting finding is that we derive a new asymptotic variance formula, providing a new perspective on the impact of pilot bandwidth and demonstrating the additional variability of the estimated bias. Based on the new theoretical results, two new pointwise confidence intervals are proposed through resampling strategies. We conduct extensive simulation studies to show that our proposed confidence intervals provide better coverage probabilities than other competitors and are not much sensitive to the choice of bandwidth. Finally, our proposed procedure is further illustrated through United States’natality birth data in 2017.

报告人简介：

郭绍俊，中国人民大学统计与大数据研究院副教授。2003年本科毕业于山东师范大学，2008年获得中国科学院数学与系统科学研究院理学博士学位。博士毕业后留中国科学院数学与系统科学研究院工作，助理研究员，任期至2016年。2009年-2010年赴美国普林斯顿大学运筹与金融工程系博士后研究，做高维数据分析方面的研究工作，并于2014-2016年在英国伦敦经济学院统计系做博士后研究，做大维时间序列建模方面的研究。目前主要研究方向有：统计学习；非参数及半参数统计建模；生存分析及函数型数据分析等。