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Activity Number:
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240
- Computationally Intensive Methods for Estimation and Inference
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #323490
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Title:
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An Effecient Method for Quantile Regression
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Author(s):
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Mei Ling Huang* and Ramona Rat and Wai Kong Yuen
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Companies:
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Brock University and Brock University and Brock University
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Keywords:
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Bivariate Pareto distribution ;
Conditional quantile ;
Extreme value distributions ;
Linear programing ;
Monte Carlo simulation ;
Weighted loss function
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Abstract:
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Quantile regression has wide applications in many fields. Studies of heavy tailed distributions have also rapidly developed. Estimation of high conditional quantiles for multivariate heavy tailed distributions is an important and interesting problem. This paper proposes a new weighted quantile regression method in high quantile regression. The Monte Carlo simulations of the bivariate Pareto distribution show good efficiency of the proposed weighted estimator relative to the regular quantile regression estimator. The paper also investigates a real-world example by using the proposed weighted method. Comparisons of the proposed method with existing methods are given.
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Authors who are presenting talks have a * after their name.
