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Mathematics > Statistics Theory

arXiv:2504.18769 (math)
[Submitted on 26 Apr 2025 ]

Title: A Simplified Condition For Quantile Regression

Title: 分位数回归的一个简化条件

Authors:Liang Peng, Yongcheng Qi
Abstract: Quantile regression is effective in modeling and inferring the conditional quantile given some predictors and has become popular in risk management due to wide applications of quantile-based risk measures. When forecasting risk for economic and financial variables, quantile regression has to account for heteroscedasticity, which raises the question of whether the identification condition on residuals in quantile regression is equivalent to one independent of heteroscedasticity. In this paper, we present some identification conditions under three probability models and use them to establish simplified conditions in quantile regression.
Abstract: 分位数回归在给定某些预测变量的情况下对条件分位数进行建模和推断,由于基于分位数的风险度量具有广泛的应用,在风险管理中变得流行起来。当预测经济和金融变量的风险时,分位数回归必须考虑异方差性,这引发了关于分位数回归残差的识别条件是否等价于与异方差无关的一个问题。本文在三种概率模型下给出了某些识别条件,并利用它们在分位数回归中建立了简化的条件。
Subjects: Statistics Theory (math.ST) ; Probability (math.PR)
MSC classes: 60E05, 62J05, 62G05
Cite as: arXiv:2504.18769 [math.ST]
  (or arXiv:2504.18769v1 [math.ST] for this version)
  https://doi.org/10.48550/arXiv.2504.18769
arXiv-issued DOI via DataCite

Submission history

From: Yongcheng Qi [view email]
[v1] Sat, 26 Apr 2025 02:15:56 UTC (10 KB)
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