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

发布者:季洁发布时间:2020-06-03浏览次数:224

Speaker

Shaojun Guo (Renmin University)

HostXuening Zhu
Time10:00-11:00 am, June 5, 2020

Zoom ID

635 9752 2129
Abstract 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.
BioCurrently Shaojun Guo is an Associate Professor in the Institute of Statistics and Big Data at Renmin University of China. Before that, he was an Assistant Professor in Academy of Mathematics and Systems Science at Chinese Academy of Sciences since 2008 and also Research Fellow in Department of Statistics at London school of Economics and Political Science from 2014 to 2016.He completed his Ph.D. in Mathematical Statistics from Academy of Mathematics and Systems Science at Chinese Academy of Sciences in 2008, advised by Professor Min Chen. From 2009 to 2010 he was a Visiting Postdoctoral Research Associate in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University, hosted by Professor Jianqing Fan.