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Nonparametric two-sample test for random objects based on data depth
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DOI：10.1080/02331888.2025.2578642.png)
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The rise of non-Euclidean data necessitates more versatile statistical tools. While two-sample testing is common, most existing methods are confined to specific data types or disparities. To address this gap, we propose an innovative, data depth-based two-sample test for random objects in metric spaces. We rigorously evaluate the test via simulation studies, examining its power and consistency against location and scale differences. Results demonstrate that our method exhibits strong finite-sample performance for vectors and probability density functions, often proving superior or comparable to existing techniques in effectiveness.
Keyword:
Data depth
density function data
two-sample test
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1
论文数:
81
被引数:
0
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引用论文
New Nonparametric Tests for Comparing Multivariate Scales Using Data Depth利用数据深度的多元尺度比较新非参数检验方法
PREAI
分享收藏METRIC STATISTICS: EXPLORATION AND INFERENCE FOR RANDOM OBJECTS WITH DISTANCE PROFILES.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
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