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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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High quantile regression for extreme events.

Mei Ling Huang1, Christine Nguyen1

  • 1Department of Mathematics & Statistics, Brock University, St. Catharines, Ontario, Canada.

Journal of Statistical Distributions and Applications
|February 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a weighted quantile regression method for accurately estimating extreme event quantiles in heavy-tailed distributions. The new approach improves upon existing methods, as shown by simulations and real-world data analysis.

Keywords:
Bivariate Pareto distributionConditional quantileExtreme value distributionGeneralized Pareto distributionLinear programmingWeighted loss function

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Area of Science:

  • Statistics
  • Extreme Value Theory

Background:

  • Estimating high conditional quantiles for heavy-tailed distributions is crucial for analyzing extreme events.
  • Quantile regression, utilizing an L1-loss function and linear programming, is a key method in this area.

Purpose of the Study:

  • To propose a novel weighted quantile regression method.
  • To enhance the accuracy of estimating high conditional quantiles for extreme events.

Main Methods:

  • Development of a weighted quantile regression technique.
  • Comparison with existing methods using Monte Carlo simulations.
  • Application to two real-world case studies.

Main Results:

  • The proposed weighted quantile regression method demonstrates improvement over existing techniques.
  • Simulations and real-world examples validate the enhanced performance.

Conclusions:

  • The weighted quantile regression method offers a significant advancement for analyzing extreme events.
  • This method provides more accurate estimations of high conditional quantiles.