Scholarlink

<p>In this paper, we propose a novel approach to the multiple-sample testing problem using a studentized test statistic based on the random lifter technique. The method reformulates the classical <i>K</i>-sample test as an independence test between two random variables, enabling more efficient handling of complex data types. We solve problems with the non-standard normal limiting distributions of degenerate <i>U</i>-statistics using a random lifter approach. This creates a test statistic that is asymptotically normal under the null hypothesis. Numerous simulations and real-world applications have demonstrated that our method performs well with many data types, including Euclidean, directional, and symmetric positive definite data. It is also very good at controlling Type I errors. Our method also shows significant computational efficiency, outperforming existing <i>K</i>-sample tests, particularly when applied to large datasets. These results suggest that the proposed method is a powerful and practical solution for multiple-sample testing in complex data scenarios.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

K-sample studentized tests: Random lifter approach

  • 期刊论文

  • 发表日期：

    2025年9月11日

查看全文
  • Roulin Wang,
  • Baisuo Jin,
  • Zhe Gao,
  • Xueqin Wang

摘要

In this paper, we propose a novel approach to the multiple-sample testing problem using a studentized test statistic based on the random lifter technique. The method reformulates the classical K-sample test as an independence test between two random variables, enabling more efficient handling of complex data types. We solve problems with the non-standard normal limiting distributions of degenerate U-statistics using a random lifter approach. This creates a test statistic that is asymptotically normal under the null hypothesis. Numerous simulations and real-world applications have demonstrated that our method performs well with many data types, including Euclidean, directional, and symmetric positive definite data. It is also very good at controlling Type I errors. Our method also shows significant computational efficiency, outperforming existing K-sample tests, particularly when applied to large datasets. These results suggest that the proposed method is a powerful and practical solution for multiple-sample testing in complex data scenarios.