Statistical Reliability of Data-Driven Science and Technology

IF 1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-01-21 DOI:10.1002/tee.24262

Ichiro Takeuchi

{"title":"Statistical Reliability of Data-Driven Science and Technology","authors":"Ichiro Takeuchi","doi":"10.1002/tee.24262","DOIUrl":null,"url":null,"abstract":"<p>With the rapid development of AI and machine learning, the use of data-driven approaches has been expanding across various fields of science and technology. In data-driven approaches, unlike traditional scientific research and technological development, hypotheses are generated based on data, requiring the consideration of data dependency when evaluating hypotheses. As a result, conventional statistical tests, which have served as the foundation for reliability assessments in scientific research and technological development, are inadequate for properly evaluating the reliability of data-driven hypotheses. In this paper, we introduce the framework known as <i>selective inference</i>, which has gained attention as a statistical reliability evaluation method for data-driven science and technology. We provide an overview of recent research trends in selective inference and present our recent studies on statistical tests for deep learning models based on selective inference. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"20 5","pages":"668-675"},"PeriodicalIF":1.0000,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tee.24262","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEJ Transactions on Electrical and Electronic Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/tee.24262","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

With the rapid development of AI and machine learning, the use of data-driven approaches has been expanding across various fields of science and technology. In data-driven approaches, unlike traditional scientific research and technological development, hypotheses are generated based on data, requiring the consideration of data dependency when evaluating hypotheses. As a result, conventional statistical tests, which have served as the foundation for reliability assessments in scientific research and technological development, are inadequate for properly evaluating the reliability of data-driven hypotheses. In this paper, we introduce the framework known as selective inference, which has gained attention as a statistical reliability evaluation method for data-driven science and technology. We provide an overview of recent research trends in selective inference and present our recent studies on statistical tests for deep learning models based on selective inference. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Abstract Image

查看原文本刊更多论文

数据驱动科学技术的统计可靠性

随着人工智能和机器学习的快速发展，数据驱动方法的使用已经扩展到各个科学和技术领域。在数据驱动方法中，与传统的科学研究和技术发展不同，假设是基于数据产生的，在评估假设时需要考虑数据依赖性。因此，作为科学研究和技术发展中可靠性评估基础的传统统计检验不足以正确评估数据驱动的假设的可靠性。在本文中，我们介绍了一种被称为选择性推理的框架，它作为一种数据驱动的科学技术的统计可靠性评估方法而受到关注。我们概述了选择性推理的最新研究趋势，并介绍了我们最近在基于选择性推理的深度学习模型的统计测试方面的研究。©2025日本电气工程师协会和Wiley期刊有限责任公司。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEJ Transactions on Electrical and Electronic Engineering 工程技术-工程：电子与电气

CiteScore

2.70

自引率

10.00%

发文量

199

审稿时长

4.3 months

期刊介绍： IEEJ Transactions on Electrical and Electronic Engineering (hereinafter called TEEE ) publishes 6 times per year as an official journal of the Institute of Electrical Engineers of Japan (hereinafter "IEEJ"). This peer-reviewed journal contains original research papers and review articles on the most important and latest technological advances in core areas of Electrical and Electronic Engineering and in related disciplines. The journal also publishes short communications reporting on the results of the latest research activities TEEE ) aims to provide a new forum for IEEJ members in Japan as well as fellow researchers in Electrical and Electronic Engineering from around the world to exchange ideas and research findings.