The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
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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
Citations
Cites
Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
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Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011) · Genome Biology · doi:10.1186/gb-2011-12-6-r60 · Cited by 16,192
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Mann, H. B., & Whitney, D. R. (1947) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730491 · Cited by 14,320
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Kruskal, W. H., & Wallis, W. A. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483441 · Cited by 13,912
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Hoeffding, W. (1994) · Springer series in statistics · doi:10.1007/978-1-4612-0865-5_26 · Cited by 6,683
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Hoeffding, W. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500830 · Cited by 4,869
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Gehan, E. A. (1965) · Biometrika · doi:10.1093/biomet/52.1-2.203 · Cited by 3,407
Asymptotically Efficient Rank Invariant Test Procedures
Peto, R., & Peto, J. (1972) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344317 · Cited by 3,393
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342
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Mantel, N. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500879 · Cited by 2,899
Link prediction in complex networks: A survey
Lü, L., & Zhou, T. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.11.027 · Cited by 2,863
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2161285 · Cited by 2,877
Barst, R. J., Rubin, L. J., Long, W. A., McGoon, M. D., Rich, S., Badesch, D. B., Groves, B. Μ., Tapson, V. F., Bourge, R. C., Brundage, B. H., et al. (1996) · New England Journal of Medicine · doi:10.1056/nejm199602013340504 · Cited by 2,688
Gene expression correlates of clinical prostate cancer behavior
Singh, D., Febbo, P. G., Ross, K. N., Jackson, D. G., Manola, J. B., Ladd, C., Tamayo, P., Renshaw, A. A., D'Amico, A. V., Richie, J. P., et al. (2002) · Cancer Cell · doi:10.1016/s1535-6108(02)00030-2 · Cited by 2,516
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Singh, D., & Singh, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105524 · Cited by 1,820
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Communication, S., Been-chian Chien, A., Jung Yi Lin, A., Tzung-pei Hong, B., & Com/locate/eswa, W. E. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.10.010 · Cited by 1,670
O’Sullivan, B., Davis, A. M., TURCOTTE, R. E., Bell, R. S., Catton, C., Chabot, P., Wunder, J. S., Kandel, R. A., Goddard, K., Sadura, A., et al. (2002) · The Lancet · doi:10.1016/s0140-6736(02)09292-9 · Cited by 1,602
Dominance statistics: Ordinal analyses to answer ordinal questions.
Cliff, N. (1993) · Psychological Bulletin · doi:10.1037/0033-2909.114.3.494 · Cited by 1,542
Michard, F., Boussat, S., CHEMLA, D., et al. (2000) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/ajrccm.162.1.9903035 · Cited by 1,459
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Reuter, M., Rosas, H. D., & Fischl, B. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.020 · Cited by 1,443
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Truong, C., Oudre, L., & Vayatis, N. (2019) · Signal Processing · doi:10.1016/j.sigpro.2019.107299 · Cited by 1,594
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Beck, R. W., Cleary, P. A., Anderson, M. M., Keltner, J. L., Shults, W. T., Kaufman, D. I., Buckley, E. G., Corbett, J. J., Kupersmith, M. J., Miller, N. R., et al. (1992) · New England Journal of Medicine · doi:10.1056/nejm199202273260901 · Cited by 1,349
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Rorden, C., Karnath, H., & Bonilha, L. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.7.1081 · Cited by 1,347
Chemotherapy Followed by Surgery Compared with Surgery Alone for Localized Esophageal Cancer
Kelsen, D. P., Ginsberg, R., Pajak, T. F., Sheahan, D. G., Gunderson, L., Mortimer, J., Estes, N., Haller, D. G., Ajani, J., Kocha, W., et al. (1998) · New England Journal of Medicine · doi:10.1056/nejm199812313392704 · Cited by 1,343
ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features
Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01061.x · Cited by 1,274
Khishe, M., & Mosavi, M. R. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113338 · Cited by 1,259
National Cooperative Crohn's Disease Study: Results of drug treatment
Summers, R. W., Switz, D. M., Sessions, J. T., Becktel, J. M., Best, W. R., Kern, F., & Singleton, J. W. (1979) · Gastroenterology · doi:10.1016/0016-5085(79)90385-8 · Cited by 1,238
Dhiman, G., & Kumar, V. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.11.024 · Cited by 1,220
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Wilcoxon, F. (1950) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1950.tb53974.x · Cited by 1,198
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Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162
Nachar, N. (2008) · Tutorials in Quantitative Methods for Psychology · doi:10.20982/tqmp.04.1.p013 · Cited by 1,358
Kharin, V. V., Zwiers, F. W., Zhang, X., & Hegerl, G. C. (2007) · Journal of Climate · doi:10.1175/jcli4066.1 · Cited by 1,103
Fay, M. P., & Proschan, M. A. (2010) · Statistics Surveys · doi:10.1214/09-ss051 · Cited by 1,194
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Madsen, B. E., & Browning, S. R. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000384 · Cited by 1,113
Small, E. J., Schellhammer, P. F., Higano, C. S., Redfern, C. H., Nemunaitis, J. J., Valone, F. H., Verjee, S., Jones, L. A., & Hershberg, R. M. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2005.04.5252 · Cited by 1,108
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Mirjalili, S., & Lewis, A. (2012) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2012.09.002 · Cited by 1,109
Flam, M. S., John, M., Pajak, T. F., et al. (1996) · Journal of Clinical Oncology · doi:10.1200/jco.1996.14.9.2527 · Cited by 1,095
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Mair, P., & Wilcox, R. R. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01246-w · Cited by 1,089
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Johnson, C. D., Esquela-Kerscher, A., Stefani, G., Byrom, M., Kelnar, K., Ovcharenko, D., Wilson, M., Wang, X., Shelton, J., Shingara, J., et al. (2007) · Cancer Research · doi:10.1158/0008-5472.can-07-1083 · Cited by 1,224
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Banker, R. D. (1984) · European Journal of Operational Research · doi:10.1016/0377-2217(84)90006-7 · Cited by 1,025
Ye, L., & Keogh, E. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557122 · Cited by 992
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Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 1,007
Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response
Spencer, C. N., McQuade, J. L., Gopalakrishnan, V., McCulloch, J. A., Vetizou, M., Cogdill, A. P., Khan, M. A. W., Zhang, X., White, M. G., Peterson, C. B., et al. (2021) · Science · doi:10.1126/science.aaz7015 · Cited by 966
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Nili, H., Wingfield, C., Walther, A., Su, L., Marslen‐Wilson, W. D., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003553 · Cited by 914
McKnight, P. E., & Najab, J. (2010) · The Corsini Encyclopedia of Psychology · doi:10.1002/9780470479216.corpsy0524 · Cited by 943
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Malchow, H. A., Ewe, K., Brandes, J. W., Goebell, H., Ehms, H., Sommer, H., & Jesdinsky, H. J. (1984) · Gastroenterology · doi:10.1016/0016-5085(84)90409-8 · Cited by 931
Woolson, R. F. (2008) · Wiley Encyclopedia of Clinical Trials · doi:10.1002/9780471462422.eoct979 · Cited by 925
Rosmond, R., Dallman, M. F., & Björntorp, P. A. (1998) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.83.6.4843 · Cited by 959
Appearance-based gaze estimation in the wild
Zhang, X., Sugano, Y., Fritz, M., & Bulling, A. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299081 · Cited by 891
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Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00560-7 · Cited by 819
The Wellcome Trust Case Control Consortium (2010) · Nature · doi:10.1038/nature08979 · Cited by 807
Detecting Change in Data Streams
Kifer, D., Ben-David, S., & Gehrke, J. E. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50019-x · Cited by 809
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(1991) · The American Journal of Medicine · doi:10.1016/0002-9343(91)80085-z · Cited by 807
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Kerby, D. S. (2014) · Comprehensive Psychology · doi:10.2466/11.it.3.1 · Cited by 806
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Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.01.017 · Cited by 787
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Yuan, Y., Xu, H., Wang, B., & Yao, X. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2420112 · Cited by 754
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Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., & Zhang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.16960 · Cited by 749
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Côté‐Allard, U., Fall, C. L., Drouin, A., Campeau‐Lecours, A., Gosselin, C., Glette, K., Laviolette, F., & Gosselin, B. J. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2896269 · Cited by 789
Grootswagers, T., Wardle, S. G., & Carlson, T. A. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01068 · Cited by 712
Error analysis of continuous GPS position time series
Williams, S. D. P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R. M., Prawirodirdjo, L., Miller, M. M., & Johnson, D. J. (2004) · Journal of Geophysical Research Atmospheres · doi:10.1029/2003jb002741 · Cited by 737
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Smucker, M. D., Allan, J., & Carterette, B. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321528 · Cited by 728
Linear rank tests with right censored data
Prentice, R. L. (1978) · Biometrika · doi:10.1093/biomet/65.1.167 · Cited by 732
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Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.070 · Cited by 664
Davis, A. M., O’Sullivan, B., TURCOTTE, R., Bell, R., Catton, C., Chabot, P., Wunder, J. S., HAMMOND, A., Benk, V., & Kandel, R. A. (2005) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2004.12.020 · Cited by 750
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Elkin, L. A., Kay, M., Higgins, J. J., & Wobbrock, J. O. (2021) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3472749.3474784 · Cited by 711
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
García, S., Derrac, J., Cano, J.-R., et al. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.142 · Cited by 697
Mirjalili, S., Mirjalili, S. M., & Yang, X. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1525-5 · Cited by 686
Paparrizos, J., & Gravano, L. (2015) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2723372.2737793 · Cited by 683
Allen, M. S., Darling, G. E., Pechet, T. T. V., et al. (2006) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2005.06.066 · Cited by 672
Skill of Real-Time Seasonal ENSO Model Predictions during 2002–11: Is Our Capability Increasing?
Barnston, A. G., Tippett, M. K., L’Heureux, M. L., Li, S., & DeWitt, D. G. (2011) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00111.1 · Cited by 671
Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review
Alfeilat, H. A., Hassanat, A. B. A., Lasassmeh, O., et al. (2019) · Big Data · doi:10.1089/big.2018.0175 · Cited by 670
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data
Song, Q., Ni, J., & Wang, G. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.181 · Cited by 664
Clinical and biologic implications of recurrent genomic aberrations in myeloma
Fonseca, R., Blood, E., Rué, M., Harrington, D. P., Oken, M. M., Kyle, R. A., Dewald, G. W., Ness, B. V., Wier, S. A. V., Henderson, K. J., et al. (2003) · Blood · doi:10.1182/blood-2002-10-3017 · Cited by 659
Gait Assessment in Parkinson's Disease: Toward an Ambulatory System for Long-Term Monitoring
Salarian, A., Russmann, H., Vingerhoets, F. J. G., Dehollain, C., Blanc, Y., Burkhard, P. R., & Aminian, K. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827933 · Cited by 658
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Fu, Y., Jung, A. W., Viñas, R., González, S., Vöhringer, H. S., Shmatko, A., Yates, L. R., Jimenez‐Liñan, M., Moore, L., & Gerstung, M. (2020) · Nature Cancer · doi:10.1038/s43018-020-0085-8 · Cited by 655
Ouimet, C. C., Miller, P. E., Hemmings, H. C., Walaas, S. I., & Greengard, P. (1984) · Journal of Neuroscience · doi:10.1523/jneurosci.04-01-00111.1984 · Cited by 655
Properties of Human IgG1s Engineered for Enhanced Binding to the Neonatal Fc Receptor (FcRn)
Dall’Acqua, W. F., Kiener, P. A., & Wu, H. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m604292200 · Cited by 648
Event Study Testing with Cross-sectional Correlation of Abnormal Returns
Kolari, J. W., & Pynnönen, S. (2010) · Review of Financial Studies · doi:10.1093/rfs/hhq072 · Cited by 649
Decision trees for hierarchical multi-label classification
Vens, C., Struyf, J., Schietgat, L., Džeroski, S., & Blockeel, H. (2008) · Machine Learning · doi:10.1007/s10994-008-5077-3 · Cited by 640
SURGICAL MANAGEMENT OF RENAL TUMORS 4 CM. OR LESS IN A CONTEMPORARY COHORT
Lee, C. T., Katz, J., Shi, W., Thaler, H. T., Reuter, V. E., & Russo, P. (2000) · The Journal of Urology · doi:10.1016/s0022-5347(05)67793-2 · Cited by 637
A time series forest for classification and feature extraction
Deng, H., Runger, G. C., Tuv, E., & Vladimir, M. (2013) · Information Sciences · doi:10.1016/j.ins.2013.02.030 · Cited by 632
Probiotics modulated gut microbiota suppresses hepatocellular carcinoma growth in mice
Li, J., Sung, C. Y. J., Lee, N. P. Y., Ni, Y., Pihlajamäki, J., Panagiotou, G., & El-Nezami, H. S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1518189113 · Cited by 632
Sun, D., Roth, S., & Black, M. J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0644-x · Cited by 698
Islam, S. M., Das, S., Ghosh, S., Roy, S., & Suganthan, P. N. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2167966 · Cited by 609
Chicco, D., & Jurman, G. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1023-5 · Cited by 664
(1992) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9 · Cited by 616
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
Zhang, X., Sugano, Y., Fritz, M., & Bulling, A. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2778103 · Cited by 627
Reduced and oxidized glutathione in the substantia nigra of patients with Parkinson's disease
Sofic, E., Lange, K. W., Jellinger, K. A., & Riederer, P. (1992) · Neuroscience Letters · doi:10.1016/0304-3940(92)90355-b · Cited by 591
Mapping the stereotyped behaviour of freely moving fruit flies
Berman, G. J., Choi, D. M., Bialek, W., & Shaevitz, J. W. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0672 · Cited by 565
Probability Tables for Individual Comparisons by Ranking Methods
Wilcoxon, F. (1947) · Biometrics · doi:10.2307/3001946 · Cited by 578
Self-citationLink prediction based on local random walk
Liu, W., & Lü, L. (2010) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/89/58007 · Cited by 580
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Cuevas, E., Cienfuegos, M., Zaldívar, D., & Pérez‐Cisneros, M. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.041 · Cited by 580
Back disorders and nonneutral trunk postures of automobile assembly workers.
Punnett, L., Fine, L. J., Keyserling, W. M., Herrin, G. D., & Chaffin, D. B. (1991) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.1700 · Cited by 575
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Whyte, M. P., Greenberg, C. R., Salman, N., Bober, M. B., McAlister, W. H., Wenkert, D., Sickle, B. J. V., Simmons, J. H., Edgar, T. S., Bauer, M. L., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1106173 · Cited by 564
The Wilcoxon signed rank test for paired comparisons of clustered data
Rosner, B., Glynn, R. J., & Lee, M. T. (2006) · Biometrics · doi:10.1111/j.1541-0420.2005.00389.x · Cited by 557
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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