Testing heteroskedasticity in trace regression with low-rank matrix parameter

被引：0
|
作者：
Tan, Xiangyong [1 ,2 ]
Lu, Xuanliang [1 ]
Hu, Tianying [1 ,2 ]
Li, Hongmei [1 ]
机构：
[1] Jiangxi Univ Finance & Econ, Sch Stat & Data Sci, Nanchang 330013, Jiangxi, Peoples R China
[2] Jiangxi Univ Finance & Econ, Key Lab Data Sci Finance & Econ, Nanchang, Jiangxi, Peoples R China
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2025年
基金：
中国博士后科学基金;
关键词：
Heteroskedasticity; Trace regression model; Low-rank; C22; XXX; HETEROSCEDASTICITY; VARIANCE;
D O I：
10.1080/03610926.2025.2472791
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Heteroskedasticity testing is crucial in regression analysis, yet research on heteroskedasticity tests for matrix data remains limited. This article introduces a novel approach for testing heteroskedasticity in trace regression, using the nuclear norm penalty to account for the low-rank structure of the unknown parameters. Under some mild conditions and the null hypothesis, we derive the asymptotic distribution of the test statistic. Both simulation results and analyses of real data demonstrate that the proposed testing procedure performs well.
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收藏
页数：14
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