HDLSSkST
2.1.0Distribution-Free Exact High Dimensional Low Sample Size k-Sample Tests
Overview
Testing homogeneity of k multivariate distributions is a classical and challenging problem in statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size. We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. These tests are easy to implement and distribution-free in finite sample situations. Under appropriate regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2021) doi:10.1016/j.jmva.2021.104897; Soham Sarkar and Anil K Ghosh (2019) doi:10.1109/TPAMI.2019.2912599; William M Rand (1971) doi:10.1080/01621459.1971.10482356; Cyrus R Mehta and Nitin R Patel (1983) doi:10.2307/2288652; Joseph C Dunn (1973) doi:10.1080/01969727308546046; Sture Holm (1979) doi:10.2307/4615733; Yoav Benjamini and Yosef Hochberg (1995) <doi: 10.2307/2346101>.
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Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
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- References docs
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Package metadata
- First published
- 2020-08-04
- Total releases
- 4 / 6 yrs
- License
- GPL (>= 2) OSI
- Download size
- 15 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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