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首页> 外文期刊>Journal of applied statistics >Extreme values identification in regression using a peaks-over-threshold approach
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Extreme values identification in regression using a peaks-over-threshold approach

机译：使用峰值阈值法进行回归中的极值识别

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摘要

The problem of heavy tail in regression models is studied. It is proposed that regression models are estimated by a standard procedure and a statistical check for heavy tail using residuals is conducted as a tool for regression diagnostic. Using the peaks-over-threshold approach, the generalized Pareto distribution quantifies the degree of heavy tail by the extreme value index. The number of excesses is determined by means of an innovative threshold model which partitions the random sample into extreme values and ordinary values. The overall decision on a significant heavy tail is justified by both a statistical test and a quantile-quantile plot. The usefulness of the approach includes justification of goodness of fit of the estimated regression model and quantification of the occurrence of extremal events. The proposed methodology is supplemented by surface ozone level in the city center of Leeds.
机译：研究了回归模型中的重尾问题。建议通过标准程序估算回归模型，并使用残差对重尾进行统计检查，以作为回归诊断的工具。使用峰值阈值方法，广义的帕累托分布通过极值指标量化了重尾的程度。多余的数量通过创新的阈值模型确定，该模型将随机样本分为极值和普通值。统计检验和分位数-分位数图都证明了对明显的重尾巴的总体决策是合理的。该方法的有用性包括估计回归模型的拟合优度的证明和极端事件发生的量化。利兹市中心的地表臭氧水平补充了所建议的方法。

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