報告題目：How Asymptotics Meets Application：Better Nonparametric Confidence Intervals for Quantile Regression
報 告 人：郭紹俊 中國人民大學 副教授
報告時間：2020年6月24日 下午 13:30-14:30
報告地點：騰訊會議
點擊鍊接入會，或添加至會議列表：
https://meeting.tencent.com/s/7A7KNOrcOlWi
會議 ID：253 244 489
會議密碼：0624
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：
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.
報告人簡介：
郭紹俊，中國人民大學統計與大數據研究院副教授。2003年本科畢業于山東師範大學，2008年獲得中國科學院數學與系統科學研究院理學博士學位。博士畢業後留中國科學院數學與系統科學研究院工作，助理研究員，任期至2016年。2009年-2010年赴美國普林斯頓大學運籌與金融工程系博士後研究，做高維數據分析方面的研究工作，并于2014-2016年在英國倫敦經濟學院統計系做博士後研究，做大維時間序列建模方面的研究。目前主要研究方向有：統計學習；非參數及半參數統計建模；生存分析及函數型數據分析等。
