Model-Free Statistical Inference on High-Dimensional Data*

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-01-26 DOI:10.1080/01621459.2024.2310314

Xu Guo, Runze Li, Zhe Zhang, Changliang Zou

引用次数: 0

Abstract

This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. W...

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高维数据的无模型统计推断*

本文旨在为高维数据开发一种有效的无模型推断程序。我们首先通过充分降维框架来重新表述假设检验问题。W...

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.