Moderate deviations for quantile regression processes

Mingzhi Mao and Wanli Guo

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 12, 2879-2892

Abstract: This paper mainly discusses the asymptotic properties of quantile regression processes. In view of the exponential tightness and convexity argument, we prove the quantile regression estimators satisfy the functional moderate deviation principle. This method can be extended to a fair range of different statistical estimation problems such as quantile regression estimators with bridge penalized functions.

Date: 2019
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DOI: 10.1080/03610926.2018.1473429

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