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Activity Number: 350
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #312800
Title: A Flexible and Super-Fast Quantile Regression Solver
Author(s): Youlan Rao*+ and Yonggang Yao
Companies: Parexel and SAS Institute
Keywords: quantile regression ; LASSO QR ; elastic-net QR ; simulation ; real application
Abstract:

We present a unified algorithm framework for solving Quantile Regression(QR), LASSO QR, and elastic-net QR. This method smoothies check loss, LASSO and elastic-net penalty with their linear-quadratic approximation substitutes, so that these types of QR problems can be solved by using iterative least squares method. Because the solution of a QR problem only relies on a small support subset of all the observations, our algorithm could solve a QR problem faster than the ordinary-least-squares method for linear regression for a given data set. We demonstrate the performance of this new algorithm with a simulation study and a real patients' data analysis in a clinical trial.


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