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<p>Functional data analysis (FDA) continues to gain prominence in modern statistical research. Building on the concept of statistical depth, originally developed for multivariate data, this paper extends its application to functional settings and proposes several nonparametric procedures for testing the equality of mean functions across multiple functional populations. The proposed tests are implemented within a permutation-based framework. Their performance is evaluated through extensive simulation studies, which demonstrate superior results compared to several established alternatives. In addition, the methods are validated using multiple real-world functional datasets.</p>

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Depth-Based Nonparametric Tests for Comparing Mean Functions of Multiple Functional Samples

  • 期刊论文

  • 发表日期：

    2025年10月19日

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  • Latika Uttamrao Shinde,
  • Digambar Tukaram Shirke

摘要

Functional data analysis (FDA) continues to gain prominence in modern statistical research. Building on the concept of statistical depth, originally developed for multivariate data, this paper extends its application to functional settings and proposes several nonparametric procedures for testing the equality of mean functions across multiple functional populations. The proposed tests are implemented within a permutation-based framework. Their performance is evaluated through extensive simulation studies, which demonstrate superior results compared to several established alternatives. In addition, the methods are validated using multiple real-world functional datasets.