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In the citing paper
“In TP learning, specific task relevant parameters are defined, such as object positions and orientations in an environment, and these are used to construct frames of reference.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [4]
In the citing paper
“By learning task representations in these local frames of reference, the learner's extrapolation abilities can be improved a great deal.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [4]
In the citing paper
“Significant improvement using the proposed method is shown in a test data set for a reaching task, versus the original TP-GMR method described in [4] and a modified TP method described in [5], specifically designed to improve extrapolation of learned skills.”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [4]
In the citing paper
“Many learning methods will perform well under interpolation conditions, but then degrade in performance under extrapolation [4].”
From Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation (2019) · cites this paper as [4]
In the citing paper
“Given that the small sample size (n = 7) does not allow us to assume the normality of the data distribution, we used the Wilcoxon signed-rank test (Wilcoxon, 1945), a non-parametric alternative to the one-sample ttest, to analyze the preliminary efficacy outcomes.”
From Feasibility, Acceptability and Preliminary Efficacy of Dialectical Behavior Therapy for Autistic Adults without Intellectual Disability: A Mixed Methods Study (2022) · cites this paper as Wilcoxon, 1945
In the citing paper
“The Wilcoxon signed-rank test makes use of such a rank and hence, while it does not consider the evaluation measure values, it is more powerful than the sign test and its variants.”
From The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing (2018) · cites this paper as Wilcoxon, 1945
In the citing paper
“Once we have established that a statistical difference exists within the classifiers' performance, we followed the pairwise post-hoc analysis recommended by Benavoli et al. (2016) where the average rank comparison is replaced by a Wilcoxon signed-rank test (Wilcoxon, 1945) with Holm's alpha correction (Holm, 1979; Garcia and Herrera, 2008).”
From Deep learning for time series classification: a review (2019) · cites this paper as Wilcoxon, 1945
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Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
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Abedini, M., Ghasemian, B., Shirzadi, A., Shahabi, H., Chapi, K., Pham, B. T., Ahmad, B. B., & Bui, D. T. (2018) · Geocarto International · doi:10.1080/10106049.2018.1499820 · Cited by 139
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Ratasich, D., Khalid, F., Geißler, F., Grosu, R., Shafique, M., & Bartocci, E. (2019) · IEEE Access · doi:10.1109/access.2019.2891969 · Cited by 151
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Li, S., Tang, L., & Ng, S.-H. (2010) · Journal of Quality Technology · doi:10.1080/00224065.2010.11917817 · Cited by 137
FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography
Rahimian, E., Zabihi, S., Asif, A. A., Farina, D., Atashzar, S. F., & Mohammadi, A. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3077413 · Cited by 135
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Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab564 · Cited by 137
Matoušová, I., Trojovský, P., Dehghani, M., Trojovská, E., & Kostra, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-37537-8 · Cited by 210
Nonparametric Model Discrimination in International Relations
Clarke, K. A. (2003) · Journal of Conflict Resolution · doi:10.1177/0022002702239512 · Cited by 132
Molecular Docking Screens Using Comparative Models of Proteins
Fan, H., Irwin, J. J., Webb, B. M., Klebe, G., Shoichet, B. K., & Sali, A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9003706 · Cited by 135
The Effects of Training on Own and Co‐worker Productivity: Evidence from a Field Experiment
de Grip, A., & Sauermann, J. (2012) · The Economic Journal · doi:10.1111/j.1468-0297.2012.02500.x · Cited by 136
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Wolfe, D. A. (2012) · ISRN Probability and Statistics · doi:10.5402/2012/568385 · Cited by 136
Domain Adaptation of Deformable Part-Based Models
Xu, J.-L., Ramos, S., Vázquez, D., & López, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2327973 · Cited by 153
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Ren, X., Xing, Z., Xia, X., Lo, D., Wang, X., & Grundy, J. (2019) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3324916 · Cited by 143
Rico-Juan, J. R., & Paz, P. T. D. L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114590 · Cited by 141
Davies, T. F., Evered, D. C., Smith, B. R., Yeo, P. P., CLARK, F., & Hall, R. (1977) · The Lancet · doi:10.1016/s0140-6736(77)92719-2 · Cited by 135
What Hypotheses do “Nonparametric” Two-Group Tests Actually Test?
Conroy, R. M. (2012) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1201200202 · Cited by 137
Parental stress, coping strategies and social support in families of children with a disability
Cuzzocrea, F., Murdaca, A. M., Costa, S., Filippello, P., & Larcan, R. (2015) · Child Care in Practice · doi:10.1080/13575279.2015.1064357 · Cited by 165
Halder, U., Das, S., & Maity, D. (2012) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2217491 · Cited by 135
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Porz, N., Bauer, S., Pica, A., Schucht, P., Beck, J., Verma, R. K., Slotboom, J., Reyes, M., & Wiest, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096873 · Cited by 135
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Watters, R. D., Guenther, J., Brickner, A. E., et al. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0615 · Cited by 135
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Yang, X., & Wen, W. (2018) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2847353 · Cited by 135
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Santafé, G., Inza, I., & Lozano, J. A. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9433-y · Cited by 144
Fransson‐Hall, C., Gloria, R., Kilbom, Å., Winkel, J., Karlqvist, L., & Wiktorin, C. (1995) · Applied Ergonomics · doi:10.1016/0003-6870(95)00003-u · Cited by 135
Pluskal, T., Uehara, T., & Yanagida, M. (2012) · Analytical Chemistry · doi:10.1021/ac3000418 · Cited by 135
Impaired delayed hypersensitivity in systemic lupus erythematosus
Horwitz, D. A. (1972) · Arthritis & Rheumatism · doi:10.1002/art.1780150406 · Cited by 135
Veček, N., Mernik, M., & Črepinšek, M. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.154 · Cited by 134
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Ziv, E., Nemenman, I. M., & Wiggins, C. H. (2007) · PLoS ONE · doi:10.1371/journal.pone.0001077 · Cited by 128
Cescon, D. W., Haibe‐Kains, B., & Mak, T. W. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1424869112 · Cited by 134
Elkano, M., Galar, M., Sanz, J., et al. (2015) · Information Sciences · doi:10.1016/j.ins.2015.11.006 · Cited by 134
Zhao, H., & Wang, C. (2018) · Climate Dynamics · doi:10.1007/s00382-018-4136-0 · Cited by 139
Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach
Zawbaa, H. M., Emary, E., Groşan, C., et al. (2018) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.02.021 · Cited by 134
Chen, W., Tsangaratos, P., Ilia, I., Duan, Z., & Chen, X. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.05.312 · Cited by 135
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Wagner, J., Stuerzlinger, W., & Nedel, L. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934415 · Cited by 134
Wyche, K. E., Wang, S. S., Griendling, K. K., Dikalov, S., Austin, H. D., Rao, S., Fink, B., Harrison, D. G., & Zafari, A. M. (2004) · Hypertension · doi:10.1161/01.hyp.0000126579.50711.62 · Cited by 134
Flechner, S. M., Avery, R. K., Fisher, R., Mastroianni, B., Papajcik, D., O’Malley, K., Goormastic, M., Goldfarb, D. A., Modlin, C. S., & Novick, A. C. (1998) · Transplantation · doi:10.1097/00007890-199812270-00019 · Cited by 134
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Gallas, B. D. (2006) · Academic Radiology · doi:10.1016/j.acra.2005.11.030 · Cited by 133
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VanDam, M., Ambrose, S. E., & Moeller, M. P. (2012) · The Journal of Deaf Studies and Deaf Education · doi:10.1093/deafed/ens025 · Cited by 144
Acharya, U. R., Sudarshan, V. K., Koh, J. E. W., Martis, R. J., Tan, J. H., Oh, S. L., Muhammad, A., Hagiwara, Y., Mookiah, M. R. K., Chua, K. P., et al. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.07.003 · Cited by 135
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de Barros, R. S. M., Hidalgo, J. I. G., & Cabral, D. R. D. L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.10.051 · Cited by 133
Mantas, C. J., Castellano, J. G., Moral‐García, S., & Abellán, J. (2018) · Soft Computing · doi:10.1007/s00500-018-3628-5 · Cited by 133
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Mindikoglu, A. L., Dowling, T. C., Weir, M. R., Seliger, S. L., Christenson, R. H., & Magder, L. S. (2013) · Hepatology · doi:10.1002/hep.26556 · Cited by 132
Cantor, D. E., & Macdonald, J. R. (2008) · Journal of Operations Management · doi:10.1016/j.jom.2008.09.002 · Cited by 132
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Angellotti, M. C., Bhuiyan, S., Chen, G., & Wan, X. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm392 · Cited by 132
Hesketh, P. J., Harvey, W. H., Harker, W. G., et al. (1994) · Journal of Clinical Oncology · doi:10.1200/jco.1994.12.3.596 · Cited by 132
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Wang, H., Sun, Q., Zhao, W., Qi, L., Gu, Y., Li, P., Zhang, M., Li, Y., Liu, S., & Guo, Z. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu522 · Cited by 131
Wang, J., Weng, T., & Zhang, Q. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2821180 · Cited by 131
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Zhang, S., Zhang, X., Chan, J., & Rosso, P. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.04.006 · Cited by 139
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Jordán, A., Côté, P., Ferrarese, L., et al. (2004) · The Astrophysical Journal · doi:10.1086/422545 · Cited by 131
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Peritoneal macrophages and infertility: the association between cell number and pelvic pathology
Olive, D. L., Weinberg, J. B., & Haney, A. F. (1985) · Fertility and Sterility · doi:10.1016/s0015-0282(16)49036-9 · Cited by 131
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González‐Castro, V., Aláiz-Rodríguez, R., & Alegre, E. (2012) · Information Sciences · doi:10.1016/j.ins.2012.05.028 · Cited by 130
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Schwartz, Z., & Hiemstra, S. J. (1997) · Journal of Travel Research · doi:10.1177/004728759703600102 · Cited by 130
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Ji, X., & Xu, W. H. (2006) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1148170.1148241 · Cited by 129
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Alizadehsani, R., Hosseini, M. J., Khosravi, A., Khozeimeh, F., Roshanzamir, M., Sarrafzadegan, N., & Nahavandi, S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.05.009 · Cited by 128
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Khouri, I. F., Lee, M.-S., Saliba, R. M., Andersson, B. S., Anderlini, P. N., Couriel, D. R., Hosing, C. M., Giralt, S. A., Korbling, M. J., McMannis, J. D., et al. (2004) · Experimental Hematology · doi:10.1016/j.exphem.2003.09.021 · Cited by 129
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Bates, S. T., & García‐Pichel, F. (2008) · Environmental Microbiology · doi:10.1111/j.1462-2920.2008.01738.x · Cited by 127
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Tan, T. Y., Li, Z., Neoh, S. C., & Lim, C. P. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.042 · Cited by 128
Nhu, V., Shirzadi, A., Shahabi, H., Chen, W., Clague, J. J., Geertsema, M., Jaafari, A., Avand, M., Miraki, S., Asl, D. T., et al. (2020) · Forests · doi:10.3390/f11040421 · Cited by 127
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Piombino, P., Genovese, A., Esposito, S., Luigi Moio, B., Cutolo, P. P., Chambery, A., Severino, V., Moneta, E., Smith, D. P., Owens, S. M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085611 · Cited by 131
Coelho, L. D. S., & Mariani, V. C. (2012) · Computers & Mathematics with Applications · doi:10.1016/j.camwa.2012.05.007 · Cited by 126
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Tang, B., Cao, H., Wu, Y., Jiang, M., & Xu, H. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-s1-s1 · Cited by 126
Wang, C., Rajagovindan, R., Han, S.-M., & Ding, M. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00015 · Cited by 130
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Hsieh, P. J., Lin, B., & Lin, C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.03.005 · Cited by 126
Prognostic factors in advanced ovarian carcinoma.
Redman, J. R., Petroni, G. R., Saigo, P. E., et al. (1986) · Journal of Clinical Oncology · doi:10.1200/jco.1986.4.4.515 · Cited by 126
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Renard, F., Alonso, L., Fitts, Y., Hadjiosif, A., & Comby, J. (2019) · Remote Sensing · doi:10.3390/rs11030299 · Cited by 126
Hong, H., Tsangaratos, P., Ilia, I., Loupasakis, C., & Wang, Y. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.140549 · Cited by 125
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Liu, K., Wang, S., Koyuncu, A., Kim, K., Bissyandé, T. F., Kim, D., Wu, P., Klein, J., Mao, X., & Traon, Y. L. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380338 · Cited by 125
High-Quality Hypergraph Partitioning
Schlag, S., Heuer, T., Gottesbüren, L., Akhremtsev, Y., Schulz, C., & Sanders, P. (2022) · ACM Journal of Experimental Algorithmics · doi:10.1145/3529090 · Cited by 125
Lange, K. W., Wells, F. R., Jenner, P. G., & MARSDEN, C. D. (1993) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.1993.tb05838.x · Cited by 125
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LaTorre, A., Muelas, S., & Peña, J.-M. (2014) · Information Sciences · doi:10.1016/j.ins.2014.09.031 · Cited by 124
Tragin, M., Zingone, A., & Vaulot, D. (2017) · Environmental Microbiology · doi:10.1111/1462-2920.13952 · Cited by 124
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Brown, C. R. L., & Way, D. C. (1978) · American Journal of Orthodontics · doi:10.1016/0002-9416(78)90005-2 · Cited by 124
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BRADLEY, R. A. (1953) · Biometrics · doi:10.2307/3001630 · Cited by 124
Metadata
- Authors
- Wilcoxon, F.
- Corresponding author
- Wilcoxon, Frank
- Venue
- Biometrics Bulletin
- Publisher
- JSTOR
- Published
- 1945-12
- Volume
- 1
- Issue
- 6
- Pages
- 80
- ISSN
- 0099-4987 (ISSN-L)
- OpenAlex
- W4252684946
- Cited by
- 15,354
- References
- 1
- Citations by year
- 2026: 1806 · 2025: 931 · 2024: 803 · 2023: 760 · 2022: 826 · 2021: 1019 · 2020: 981 · 2019: 925 · 2018: 789 · 2017: 657 · 2016: 675 · 2015: 552 · 2014: 568 · 2013: 457 · 2012: 455
- FWCI
- 5.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics Bulletin (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- ranking algorithms, nonparametric comparison, rank 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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