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Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median
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“Automated mitral and tricuspid annular excursion have been explored in adult hearts by Storve et al. [7,8] using additional M-mode and ECG information to obtain the results.”
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Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567
The Influence Curve and Its Role in Robust Estimation
Hampel, F. R. (1974) · Journal of the American Statistical Association · doi:10.1080/01621459.1974.10482962 · Cited by 2,847
Alternatives to the Median Absolute Deviation
Rousseeuw, P. J., & Croux, C. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476408 · Cited by 2,672
Methods for dealing with reaction time outliers.
Ratcliff, R. (1993) · Psychological Bulletin · doi:10.1037/0033-2909.114.3.510 · Cited by 2,142
Alternatives to the Median Absolute Deviation
Rousseeuw, P. J., & Croux, C. (1993) · Journal of the American Statistical Association · doi:10.2307/2291267 · Cited by 1,489
Outliers detection and treatment: a review.
Cousineau, D., & Chartier, S. (2010) · International journal of psychological research · doi:10.21500/20112084.844 · Cited by 719
Handbook of Research Methods. in Social and Personality Psychology
Lai, H. L. T. (2000) · Cited by 629
Short Report: Reaction Time Analysis with Outlier Exclusion: Bias Varies with Sample Size
Miller, J. O. (1991) · The Quarterly Journal of Experimental Psychology Section A · doi:10.1080/14640749108400962 · Cited by 408
ORR, J. M., Sackett, P. R., & DuBois, C. L. Z. (1991) · Personnel Psychology · doi:10.1111/j.1744-6570.1991.tb02401.x · Cited by 183
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The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences
Hedge, C., Powell, G., & Sumner, P. (2018) · Behavior Research Methods · doi:10.3758/s13428-017-0935-1 · Cited by 1,457
Curtis, M. J., Alexander, S., Cirino, G., Docherty, J. R., George, C. H., Giembycz, M. A., Hoyer, D., Insel, P. A., Izzo, A. A., Ji, Y., et al. (2018) · British Journal of Pharmacology · doi:10.1111/bph.14153 · Cited by 1,338
Levshina, N. (2015) · John Benjamins Publishing Company eBooks · doi:10.1075/z.195 · Cited by 862
Characterization of cell fate probabilities in single-cell data with Palantir
Setty, M., Kiseliovas, V., Levine, J., Gayoso, A., Mazutis, L., & Pe’er, D. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0068-4 · Cited by 833
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
Sanz, H., Valim, C., Vegas, E., Oller, J. M., & Reverter, F. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2451-4 · Cited by 835
Improving fluid intelligence with training on working memory: a meta-analysis
Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-014-0699-x · Cited by 727
Generalized Out-of-Distribution Detection: A Survey
Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884
Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance
Leys, C., Klein, O., Dominicy, Y., & Ley, C. (2017) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2017.09.011 · Cited by 511
Self-citationECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C., & Chen, G. H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3159580 · Cited by 516
Preprocessing pupil size data: Guidelines and code
Kret, M. E., & Sjak-Shie, E. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1075-y · Cited by 476
Integrated global assessment of the natural forest carbon potential
Mo, L., Zohner, C. M., Reich, P. B., Liang, J., de‐Miguel, S., Nabuurs, G. J., Renner, S. S., van den Hoogen, J., Araza, A. B., Herold, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06723-z · Cited by 506
Influences of artificial intelligence (AI) awareness on career competency and job burnout
Kong, H., Yuan, Y., Baruch, Y., Bu, N. T., Jiang, X., & Wang, K. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-07-2020-0789 · Cited by 500
Pre-registration in social psychology—A discussion and suggested template
Veer, A. V. '., & Giner‐Sorolla, R. (2016) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2016.03.004 · Cited by 464
Climate change fingerprints in recent European plant phenology
Menzel, A., Yuan, Y., Matiu, M., Sparks, T. H., Scheifinger, H., Gehrig, R., & Estrella, N. (2020) · Global Change Biology · doi:10.1111/gcb.15000 · Cited by 415
Statistical downscaling of precipitation using machine learning techniques
Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., & Perera, B. J. C. (2018) · Atmospheric Research · doi:10.1016/j.atmosres.2018.05.022 · Cited by 373
DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses
Abadi, M. K., Subramanian, R., Kia, S. M., Avesani, P., Patras, I., & Sebe, N. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2392932 · Cited by 375
MIDAS robust trend estimator for accurate GPS station velocities without step detection
Blewitt, G., Kreemer, C., Hammond, W. C., & Gazeaux, J. (2016) · Journal of Geophysical Research Solid Earth · doi:10.1002/2015jb012552 · Cited by 363
High-Fat Diet Induced Anxiety and Anhedonia: Impact on Brain Homeostasis and Inflammation
Dutheil, S., Ota, K. T., Wohleb, E. S., Rasmussen, K., & Duman, R. S. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2015.357 · Cited by 355
Global warming leads to more uniform spring phenology across elevations
Vitasse, Y., Signarbieux, C., & Fu, Y. H. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717342115 · Cited by 357
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Taherkhani, M., Vitousek, S., Barnard, P. L., Frazer, L. N., Anderson, T. R., & Fletcher, C. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-62188-4 · Cited by 347
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Melton, F., Huntington, J., Grimm, R., Herring, J., Hall, M., Rollison, D., Erickson, T. A., Allen, R. G., Anderson, M. C., Fisher, J. B., et al. (2021) · JAWRA Journal of the American Water Resources Association · doi:10.1111/1752-1688.12956 · Cited by 332
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Ruggeri, K., Alì, S., Berge, M. L., Bertoldo, G., Bjørndal, L. D., Cortijos‐Bernabeu, A., Davison, C., Demić, E., Serna, C. E., Friedemann, M., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0886-x · Cited by 364
An exploratory study into the effects of extraordinary nature on emotions, mood, and prosociality
Joye, Y., & Bolderdijk, J. W. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01577 · Cited by 328
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Trugman, D. T., & Shearer, P. M. (2017) · Seismological Research Letters · doi:10.1785/0220160188 · Cited by 317
Comparison of Different Response Time Outlier Exclusion Methods: A Simulation Study
Berger, A., & Kiefer, M. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.675558 · Cited by 314
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George, A., Mostaani, Z., Geissenbuhler, D., Nikisins, O., Anjos, A., & Marcel, S. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916652 · Cited by 308
Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019) · International Review of Social Psychology · doi:10.5334/irsp.289 · Cited by 381
Self-citation33 years of globally calibrated wave height and wind speed data based on altimeter observations
Ribal, A., & Young, I. R. (2019) · Scientific Data · doi:10.1038/s41597-019-0083-9 · Cited by 295
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Tuttelberg, K., Kilter, J., Wilson, D., & Uhlen, K. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2843381 · Cited by 290
flowAI: automatic and interactive anomaly discerning tools for flow cytometry data
Monaco, G., Chen, H., Poidinger, M., Chen, J., de Magalhães, J. P., & Larbi, A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw191 · Cited by 286
Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression
Wimber, M., Alink, A., Charest, I., Kriegeskorte, N., & Anderson, M. C. (2015) · Nature Neuroscience · doi:10.1038/nn.3973 · Cited by 282
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Schmid, P., & Betsch, C. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0632-4 · Cited by 264
Recent trends in the frequency and duration of global floods
Najibi, N., & Devineni, N. (2018) · Earth System Dynamics · doi:10.5194/esd-9-757-2018 · Cited by 261
Bhagat, S. K., Tung, T. M., & Yaseen, Z. M. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119473 · Cited by 252
How to do Linguistics with R: Data exploration and statistical analysis
Levshina, N. (2015) · Cited by 250
Bulot, F. M. J., Johnston, S. J., Basford, P. J., Easton, N. H. C., Apetroaie-Cristea, M., Foster, G. L., Morris, A. K. R., Cox, S., & Loxham, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43716-3 · Cited by 242
A Multisite Preregistered Paradigmatic Test of the Ego-Depletion Effect
Vohs, K. D., Schmeichel, B. J., Lohmann, S., Gronau, Q. F., Finley, A. J., Ainsworth, S. E., Alquist, J. L., Baker, M. D., Brizi, A., Bunyi, A., et al. (2021) · Psychological Science · doi:10.1177/0956797621989733 · Cited by 233
Lightfoot, E., & O’Connell, T. C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153850 · Cited by 226
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Mourey, J. A., Olson, J. G., & Yoon, C. (2017) · Journal of Consumer Research · doi:10.1093/jcr/ucx038 · Cited by 226
Liu, Q., Wang, X., Huang, X., & Yin, X. S. (2020) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2020.103595 · Cited by 228
Vitasse, Y., Schneider, L., Rixen, C., Christen, D., & Rebetez, M. (2017) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2017.09.005 · Cited by 223
OECD (2024) · Programme for international student assessment/Internationale Schulleistungsstudie · doi:10.1787/01820d6d-en · Cited by 254
Li, X., Gentine, P., Lin, C., Zhou, S., Sun, Z., Zheng, Y., Liu, J., & Zheng, C. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.11.017 · Cited by 209
Iida, Y., Takatani, Y., Kojima, A., & Ishii, M. (2020) · Journal of Oceanography · doi:10.1007/s10872-020-00571-5 · Cited by 208
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Gagnepain, P., Hulbert, J. C., & Anderson, M. C. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2732-16.2017 · Cited by 221
Lang, C., Kalak, N., Brand, S., Holsboer‐Trachsler, E., Pühse, U., & Gerber, M. (2015) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2015.07.004 · Cited by 228
Volk, J., Huntington, J., Melton, F., Allen, R. G., Anderson, M. C., Fisher, J. B., Kiliç, A., Ruhoff, A., Senay, G. B., Minor, B., et al. (2024) · Nature Water · doi:10.1038/s44221-023-00181-7 · Cited by 200
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da Costa, D. A., McIntosh, S., Shang, W., Kulesza, U., Coelho, R. D. S., & Hassan, A. E. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2616306 · Cited by 197
Lewy body-like alpha-synuclein inclusions trigger reactive microgliosis prior to nigral degeneration
Duffy, M. F., Collier, T. J., Patterson, J. R., Kemp, C. J., Luk, K. C., Tansey, M. G., Paumier, K. L., Kanaan, N. M., Fischer, D. L., Polinski, N. K., et al. (2018) · Journal of Neuroinflammation · doi:10.1186/s12974-018-1171-z · Cited by 197
Clegg, S. M., Wiens, R. C., Anderson, R. B., Forni, O., Frydenvang, J., Lasue, J., Cousin, A., Payré, V., Boucher, T., Dyar, M. D., et al. (2016) · Spectrochimica Acta Part B Atomic Spectroscopy · doi:10.1016/j.sab.2016.12.003 · Cited by 194
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Noonan, M. P., Adamian, N., Pike, A. C., Printzlau, F. A. B., Crittenden, B. M., & Stokes, M. G. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2133-15.2016 · Cited by 211
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Thermophysical Properties of the Lennard-Jones Fluid: Database and Data Assessment
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Chen, Q., Zhang, M., & Xue, B. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2017.2683489 · Cited by 170
Yang, J., Rahardja, S., & Fränti, P. (2019) · International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC) · doi:10.1145/3371425.3371427 · Cited by 166
Mauricio, A., Smith, W. A., Randall, R. B., Antoni, J., & Gryllias, K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106891 · Cited by 163
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Antonakis, J., House, R. J., & Simonton, D. K. (2017) · Journal of Applied Psychology · doi:10.1037/apl0000221 · Cited by 159
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Gou, B., Xu, Y. H., & Feng, X. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.3029295 · Cited by 158
Vallania, F., Tam, A., Lofgren, S., Schaffert, S. A., Azad, T. D., Bongen, E., Haynes, W. A., Alsup, M., Alonso, M. N., Davis, M. M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07242-6 · Cited by 161
Song, X., Jousilahti, P., Stehouwer, C. D. A., Söderberg, S., Onat, A., Laatikainen, T. K., Yudkin, J. S., Dankner, R. S., Morris, R. W., Tuomilehto, J., et al. (2014) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2014.09.004 · Cited by 157
Global warming-induced upper-ocean freshening and the intensification of super typhoons
Balaguru, K., Foltz, G. R., Leung, L.-Y. R., & Emanuel, K. (2016) · Nature Communications · doi:10.1038/ncomms13670 · Cited by 154
Legrand, N., Nikolova, N. E., Corrêa, C. M. C., Brændholt, M., Stuckert, A., Mathiasen, N. K., Vejlø, M., Fardo, F., & Allen, M. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108239 · Cited by 162
Song, Y., Liu, Y., Wang, M., Lanaj, K., Johnson, R. E., & Shi, J. (2017) · Academy of Management Journal · doi:10.5465/amj.2016.0448 · Cited by 156
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Tian, L., Xue, B., Wang, Z., Li, D., Yao, X., Cao, Q., Zhu, Y. P., Cao, W., & Cheng, T. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112350 · Cited by 157
Iglesias, J. E., Billot, B., Balbastre, Y., Tabari, A., Conklin, J., González, R. G., Alexander, D. C., Golland, P., Edlow, B. L., & Fischl, B. R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118206 · Cited by 159
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Arora, D., Abel, N. B., Liu, C., Damme, P. V., Yperman, K., Vu, L. D., Wang, J., Törnkvist, A., Impens, F., Korbei, B., et al. (2020) · The Plant Cell · doi:10.1105/tpc.20.00235 · Cited by 143
Gu, H., Qiao, Y., Xi, Z., Rossi, S., Smith, N. G., Jian-quan, L., & Chen, L. (2022) · Nature Communications · doi:10.1038/s41467-022-31496-w · Cited by 148
Meditation awareness training for the treatment of workaholism: A controlled trial
Gordon, W. V., Shonin, E., Dunn, T. J., García‐Campayo, J., Demarzo, M., & Griffiths, M. D. (2017) · Journal of Behavioral Addictions · doi:10.1556/2006.6.2017.021 · Cited by 144
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Enshaeifar, S., Zoha, A., Markides, A., Skillman, S., Acton, S. T., Elsaleh, T., Hassanpour, M., Ahrabian, A., Kenny, M., Klein, S., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195605 · Cited by 139
Su, G.-H., Xiao, Y., You, C., Zheng, R., Zhao, S., Sun, S., Zhou, J.-Y., Lin, L., Wang, H., Shao, Z., et al. (2023) · Science Advances · doi:10.1126/sciadv.adf0837 · Cited by 140
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Hochenbaum, J., Vallis, O., & Kejariwal, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07706 · Cited by 134
Barker, M. K., Mazarico, E., Neumann, G. A., Smith, D. E., Zuber, M. T., & Head, J. W. (2021) · Planetary and Space Science · doi:10.1016/j.pss.2020.105119 · Cited by 133
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Buttlar, B., & Walther, E. (2018) · Appetite · doi:10.1016/j.appet.2018.06.011 · Cited by 134
Su, H., Shen, W., Wang, J., Ali, A., & Li, M. (2020) · Forest Ecosystems · doi:10.1186/s40663-020-00276-7 · Cited by 138
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Dapp, L. C., Gashaj, V., & Roebers, C. M. (2021) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2021.101916 · Cited by 170
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Kovacs, D. J., Li, Z., Baetz, B. W., Hong, Y., Donnaz, S., Zhao, X., Zhou, P., Ding, H. H., & Dong, Q. (2022) · Journal of Membrane Science · doi:10.1016/j.memsci.2022.120817 · Cited by 138
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Chapeton, J. I., Haque, R. U., Wittig, J. H., Inati, S. K., & Zaghloul, K. A. (2019) · Current Biology · doi:10.1016/j.cub.2019.07.014 · Cited by 136
Tham, C. Y., Tirado-Magallanes, R., Goh, Y., Fullwood, M. J., Koh, B. T. H., Wang, W., Ng, C. H., Chng, W. J., Thiéry, A. H., Tenen, D. G., et al. (2020) · Genome biology · doi:10.1186/s13059-020-01968-7 · Cited by 129
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Marron, T. R., Lerner, Y., Berant, E., Kinreich, S., Shapira‐Lichter, I., Hendler, T., & Faust, M. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.03.018 · Cited by 137
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Guo, W., Wang, L., Xing, X., Du, M., & Song, D. X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01763 · Cited by 199
Napoly, A., Grassmann, T., Meier, F., & Fenner, D. (2018) · Frontiers in Earth Science · doi:10.3389/feart.2018.00118 · Cited by 126
Wang, P., Yao, L., Luo, M., Zhou, W., Jin, X., Xu, Z., Shi, Y., Li, Y., Xu, C., Cheng, R., et al. (2021) · Cell Discovery · doi:10.1038/s41421-021-00280-3 · Cited by 129
Metadata
- Authors
- Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L.
- Affiliations
- Université Libre de Bruxelles (BE)
- Corresponding author
- Leys, Christophe
- Venue
- Journal of Experimental Social Psychology
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 49
- Issue
- 4
- Pages
- 764-766
- ISSN
- 0022-1031 (ISSN-L)
- OpenAlex
- W2040975718
- Cited by
- 4,147
- References
- 11
- Citations by year
- 2026: 498 · 2025: 443 · 2024: 393 · 2023: 413 · 2022: 439 · 2021: 447 · 2020: 393 · 2019: 340 · 2018: 276 · 2017: 216 · 2016: 145 · 2015: 106 · 2014: 33 · 2013: 2
- FWCI
- 84.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Experimental Social Psychology (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, other-oa)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- outlier detection, median absolute deviation, standard deviation, mean, statistical software implementation
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