Introducing a High-Performance SAS Procedure for Quantile Regression

• Activity Number: 640
• Type: Topic Contributed
• Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): Yonggang Yao*+
• Companies: SAS Institute
• Keywords: quantile regression ; High Performance Computing

Abstract

Quantile regression is a systematic statistical methodology for modeling conditional quantile functions of a response variable on explanatory covariate effects. Unlike linear regression or poisson regression, which exclusively focuses on the conditional mean, quantile regression is distribution-free and can anatomize the entire response distribution and examine how the covariate effects influence the shape of the response distribution over the entire range of quantile levels ?[0,1]?. This presentation introduces a new high performance SAS procedure for quantile regression: HPQUANTSELECT, which runs in both single-machine and distributed-computing environment.