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A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
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“As for comparing the classifiers over multiple datasets, we followed the recommendations in Demˇ sar (2006) and used the Friedman test (Friedman, 1940) to reject the null hypothesis.”
From Deep learning for time series classification: a review (2019) · cites this paper as Friedman, 1940
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The use of ranks to avoid the assumption of normality implicit in the analysis of variance
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The Correlation Ratio for Ranked Data
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“The sign test (Steel, 1959) or the Wilkinson signed-ranks test (Wilcoxon, 1945) can be used to compare two strategies, while the Friedman test (Friedman, 1940), the Friedman aligned-ranks test (Hodges and Lehmann, 1962), the Friedman test with Iman-Davenport correction (Iman and Davenport, 1980),…”
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“It is also used in conjunction with the null-hypothesis Friedman test [20] and the Wilcoxon-Holm post-hoc method [27, 50] to evaluate all data sets.”
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“The Friedman non-parametric significance test [72] on Dice coefficients and Hausdorff distances indicates that AdaTrain significantly improves the”
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“To measure this similarity, a Friedman test [51] was used for the comparison between three feature distributions (for example, features from matrix 256 × 256, 256 × 128 and 128 × 128 pixels), whereas the Wilcoxon test [52] was used for the comparison between two distributions (for example, features from 1.”
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“1 Friedman Test The Friedman test [12, 13] needs to rank K algorithms on each dataset with respect to the absolute value of the results given by these algorithms.”
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“Therefore, the Friedman test that was introduced by Friedman (1937 , 1940) , together with the ranked two-way analysis of variances of the suggested approach and other implemented algorithms were conducted.”
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Statistical Inference in the Non-Parametric Case
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ECG classification using three-level fusion of different feature descriptors
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Metadata
- Authors
- Friedman, M.
- Corresponding author
- Friedman, Milton
- Venue
- The Annals of Mathematical Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1940-03
- Volume
- 11
- Issue
- 1
- Pages
- 86-92
- ISSN
- 0003-4851 (ISSN-L)
- OpenAlex
- W2016944307
- Cited by
- 2,785
- References
- 4
- Citations by year
- 2026: 191 · 2025: 256 · 2024: 244 · 2023: 253 · 2022: 264 · 2021: 231 · 2020: 209 · 2019: 173 · 2018: 133 · 2017: 106 · 2016: 132 · 2015: 93 · 2014: 82 · 2013: 72 · 2012: 67
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Mathematical Statistics (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- tests of significance, rank tests, comparison of statistical tests
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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