Efficient Multiple Change Point Detection and Localization For High-dimensional Quantile Regression with Heteroscedasticity

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-08-19 DOI:10.1080/01621459.2024.2392903

Xianru Wang, Bin Liu, Xinsheng Zhang, Yufeng Liu

引用次数: 0

Abstract

Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this paper, we consider potential structural changes ...

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具有异方差的高维量子回归的高效多变化点检测和定位

数据异质性是现代统计数据分析面临的一个挑战性问题。在实践中，有不同类型的数据异质性。在本文中，我们考虑了潜在的结构变化 ...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.