Estimation of Multiple Noncrossing Quantile Regression Functions

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TITLE: Estimation of Multiple Noncrossing Quantile Regression Functions

SPEAKER: Prof. Yufeng Liu


Quantile regression is a very useful statistical tool to learn the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility to incorporate noncrossing constraints of quantile regression functions. In this talk, I will present a new multiple noncrossing quantile regression estimation technique. Both asymptotic properties and finite sample performance will be presented to illustrate usefulness of the proposed method.

Bio: Dr. Liu is an Associate Professor in the Department of Statistics and Operations Research at The University of North Carolina at Chapel Hill. He received his MS and PhD from The Ohio State University. He is the recipient of the NSF Career Award (2008). He is an associate editor of the Journal of the American Statistical Association. His research interests are in Statistical Machine Learning and Data Mining, High Dimensional Data Analysis, Nonparametric Statistics, Bioinformatics and DOE.


  • Workflow Status: Published
  • Created By: Anita Race
  • Created: 10/12/2009
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016


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