On Quantile Regression for Extremes

• Activity Number: 299
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Mei Ling Huang*+ and Yin Xu and Wai Kong Yuen
• Companies: Brock University and Brock University and Brock University
• Keywords: Bivariate Pareto distribution ; Conditional quantile ; Extreme value distribution ; Generalized Pareto distribution ; Linear programing ; Weighted loss function

Abstract

Quantile regression has wide applications in many fields. For extreme events, we use multivariate heavy tailed distributions, then estimating of conditional quantiles at very high or low tails is interest and difficult problem. Quantile regression uses an L1- loss function, and the optimal solution of linear programming for estimating coefficients of regression. This paper proposes a weighted quantile regression method on high quantile regression for certain extreme value sets. The Monte Carlo simulations show good results of the proposed weighted method. Comparisons of the proposed method and existing methods are given. The paper also investigates a real-world example of application on extreme events by using the proposed method.