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中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (5): 586-592.DOI: 10.7523/j.ucas.2020.0059

• 数学 • 上一篇    下一篇

基于符号秩的高维均值检验

刘琰1,2, 李仕明3, 张三国1,2   

  1. 1. 中国科学院大学数学科学学院, 北京 100049;
    2. 中国科学院大数据挖掘与知识管理重点实验室, 北京 100049;
    3. 首都医科大学附属北京同仁医院眼科中心, 北京 100730
  • 收稿日期:2020-04-08 修回日期:2020-11-05 发布日期:2021-06-04
  • 通讯作者: 张三国
  • 基金资助:
    Beijing Natural Science Foundation (Z190004, JQ20029), Key Program of Joint Funds of the National Natural Science Foundation of China (U19B2040), and Capital Health Research and Development of Special (2020-2-1081)

Signed-rank-based test for high dimensional mean vector

LIU Yan1,2, LI Shiming3, ZHANG Sanguo1,2   

  1. 1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
    2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100049, China;
    3. Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
  • Received:2020-04-08 Revised:2020-11-05 Published:2021-06-04
  • Supported by:
    Beijing Natural Science Foundation (Z190004, JQ20029), Key Program of Joint Funds of the National Natural Science Foundation of China (U19B2040), and Capital Health Research and Development of Special (2020-2-1081)

摘要: 研究高维情形下一样本均值检验的问题。已有的一些高维均值检验方法假设样本具有椭球等高分布。为应用到更多的分布,提出基于符号秩的均值检验统计量。所提方法是稳健的且具有刻度变换不变性。建立了所提出检验统计量的渐近性质,数值模拟表明该方法可以很好地控制第一类错误,且功效更高。还将该方法应用到眼科数据中。

关键词: 高维数据分析, 符号秩, 一样本检验, 标度不变性

Abstract: This work is concerned with tests for one-sample mean vectors under high dimensional cases. Existing high dimensional tests for mean vectors base on the assumption of elliptical distribution have been proposed recently. To extend to more distributions, we propose a signed-rank-based test. The proposed test statistic is robust and scalar-invariant. Asymptotic properties of the test statistic are established. Numerical studies show that the proposed test has a good control of the type-I error and is more efficiency. We also employ the proposed method to analyze an ophthalmic data.

Key words: high dimensional analysis, signed-rank, one-sample test, scalar-invariance

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