Outlier (English Wikipedia)

Maddala, G. S. (1992). "Outliers". Introduction to Econometrics (2nd ed.). New York: MacMillan. pp. 89. ISBN 978-0-02-374545-4. An outlier is an observation that is far removed from the rest of the observations.
Ruan, Da; Chen, Guoqing; Kerre, Etienne (2005). Wets, G. (ed.). Intelligent Data Mining: Techniques and Applications. Studies in Computational Intelligence Vol. 5. Springer. p. 318. ISBN 978-3-540-26256-5.
Peirce, Charles Sanders (1986) [1982]. "On the Theory of Errors of Observation". In Kloesel, Christian J. W.; et al. (eds.). Writings of Charles S. Peirce: A Chronological Edition. Vol. 3, 1872–1878. Bloomington, Indiana: Indiana University Press. pp. 140–160. ISBN 978-0-253-37201-7. – Appendix 21, according to the editorial note on page 515
Tukey, John W (1977). Exploratory Data Analysis. Addison-Wesley. ISBN 978-0-201-07616-5. OCLC 3058187.

books.google.com (Global: 3^rd place; English: 3^rd place)

Maddala, G. S. (1992). "Outliers". Introduction to Econometrics (2nd ed.). New York: MacMillan. pp. 89. ISBN 978-0-02-374545-4. An outlier is an observation that is far removed from the rest of the observations.

doi.org (Global: 2^nd place; English: 2^nd place)

Grubbs, F. E. (February 1969). "Procedures for detecting outlying observations in samples". Technometrics. 11 (1): 1–21. doi:10.1080/00401706.1969.10490657. An outlying observation, or "outlier," is one that appears to deviate markedly from other members of the sample in which it occurs.
Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6) e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-11.
Hodge, Victoria J.; Austin, Jim (2004), "A Survey of Outlier Detection Methodologies", Artificial Intelligence Review, 22 (2): 85–126, doi:10.1023/B:AIRE.0000045502.10941.a9, S2CID 3330313
Zimek, A.; Schubert, E.; Kriegel, H.-P. (2012). "A survey on unsupervised outlier detection in high-dimensional numerical data". Statistical Analysis and Data Mining. 5 (5): 363–387. doi:10.1002/sam.11161. S2CID 6724536.
Peirce, Benjamin (May 1877 – May 1878). "On Peirce's criterion". Proceedings of the American Academy of Arts and Sciences. 13: 348–351. doi:10.2307/25138498. JSTOR 25138498.
Knorr, E. M.; Ng, R. T.; Tucakov, V. (2000). "Distance-based outliers: Algorithms and applications". The VLDB Journal the International Journal on Very Large Data Bases. 8 (3–4): 237. doi:10.1007/s007780050006. S2CID 11707259.
Ramaswamy, S.; Rastogi, R.; Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. Proceedings of the 2000 ACM SIGMOD international conference on Management of data - SIGMOD '00. p. 427. doi:10.1145/342009.335437. ISBN 1-58113-217-4.
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4. Archived from the original (PDF) on 2015-09-23. Retrieved 2015-08-28.
Schubert, E.; Zimek, A.; Kriegel, H. -P. (2012). "Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection". Data Mining and Knowledge Discovery. 28: 190–237. doi:10.1007/s10618-012-0300-z. S2CID 19036098.
Karch, Julian D. (2023). "Outliers may not be automatically removed". Journal of Experimental Psychology: General. 152 (6): 1735–1753. doi:10.1037/xge0001357. hdl:1887/4103722. PMID 37104797. S2CID 258376426.
Bakker, Marjan; Wicherts, Jelte M. (2014). "Outlier removal, sum scores, and the inflation of the type I error rate in independent samples t tests: The power of alternatives and recommendations". Psychological Methods. 19 (3): 409–427. doi:10.1037/met0000014. PMID 24773354.
Dixon, W. J. (June 1960). "Simplified estimation from censored normal samples". The Annals of Mathematical Statistics. 31 (2): 385–391. doi:10.1214/aoms/1177705900.
Jaulin, L. (2010). "Probabilistic set-membership approach for robust regression" (PDF). Journal of Statistical Theory and Practice. 4: 155–167. doi:10.1080/15598608.2010.10411978. S2CID 16500768.
Bishop, C. M. (August 1994). "Novelty detection and Neural Network validation". IEE Proceedings - Vision, Image, and Signal Processing. 141 (4): 217–222. doi:10.1049/ip-vis:19941330 (inactive 12 July 2025).{{cite journal}}: CS1 maint: DOI inactive as of July 2025 (link)

ensta-bretagne.fr (Global: low place; English: low place)

Jaulin, L. (2010). "Probabilistic set-membership approach for robust regression" (PDF). Journal of Statistical Theory and Practice. 4: 155–167. doi:10.1080/15598608.2010.10411978. S2CID 16500768.

hdl.handle.net (Global: 66^th place; English: 34^th place)

Karch, Julian D. (2023). "Outliers may not be automatically removed". Journal of Experimental Psychology: General. 152 (6): 1735–1753. doi:10.1037/xge0001357. hdl:1887/4103722. PMID 37104797. S2CID 258376426.

jstor.org (Global: 23^rd place; English: 15^th place)

Peirce, Benjamin (May 1877 – May 1878). "On Peirce's criterion". Proceedings of the American Academy of Arts and Sciences. 13: 348–351. doi:10.2307/25138498. JSTOR 25138498.
Thompson .R. (1985). "A Note on Restricted Maximum Likelihood Estimation with an Alternative Outlier Model".Journal of the Royal Statistical Society. Series B (Methodological), Vol. 47, No. 1, pp. 53-55

lmu.de (Global: low place; English: low place)

dbs.ifi.lmu.de

Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4. Archived from the original (PDF) on 2015-09-23. Retrieved 2015-08-28.

nih.gov (Global: 5^th place; English: 5^th place)

pubmed.ncbi.nlm.nih.gov

Karch, Julian D. (2023). "Outliers may not be automatically removed". Journal of Experimental Psychology: General. 152 (6): 1735–1753. doi:10.1037/xge0001357. hdl:1887/4103722. PMID 37104797. S2CID 258376426.
Bakker, Marjan; Wicherts, Jelte M. (2014). "Outlier removal, sum scores, and the inflation of the type I error rate in independent samples t tests: The power of alternatives and recommendations". Psychological Methods. 19 (3): 409–427. doi:10.1037/met0000014. PMID 24773354.

noaa.gov (Global: 111^th place; English: 78^th place)

docs.lib.noaa.gov

Peirce, Charles Sanders (1873) [1870]. "Appendix No. 21. On the Theory of Errors of Observation". Report of the Superintendent of the United States Coast Survey Showing the Progress of the Survey During the Year 1870: 200–224.. NOAA PDF Eprint (goes to Report p. 200, PDF's p. 215).

nrc.gov (Global: 5,536^th place; English: 3,469^th place)

E178: Standard Practice for Dealing With Outlying Observations

ox.ac.uk (Global: 541^st place; English: 356^th place)

stats.ox.ac.uk

Ripley, Brian D. 2004. Robust statistics Archived 2012-10-21 at the Wayback Machine

projecteuclid.org (Global: 4,781^st place; English: 3,117^th place)

Dixon, W. J. (June 1960). "Simplified estimation from censored normal samples". The Annals of Mathematical Statistics. 31 (2): 385–391. doi:10.1214/aoms/1177705900.

psyarxiv.com (Global: low place; English: low place)

Karch, Julian D. (2023). "Outliers may not be automatically removed". Journal of Experimental Psychology: General. 152 (6): 1735–1753. doi:10.1037/xge0001357. hdl:1887/4103722. PMID 37104797. S2CID 258376426.

qualitydigest.com (Global: low place; English: low place)

Wheeler, Donald J. (11 January 2021). "Some Outlier Tests: Part 2". Quality Digest. Retrieved 2025-02-09.

sdu.dk (Global: low place; English: low place)

findresearcher.sdu.dk

Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6) e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-11.

semanticscholar.org (Global: 15^th place; English: 8^th place)

api.semanticscholar.org

Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6) e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-11.
Hodge, Victoria J.; Austin, Jim (2004), "A Survey of Outlier Detection Methodologies", Artificial Intelligence Review, 22 (2): 85–126, doi:10.1023/B:AIRE.0000045502.10941.a9, S2CID 3330313
Zimek, A.; Schubert, E.; Kriegel, H.-P. (2012). "A survey on unsupervised outlier detection in high-dimensional numerical data". Statistical Analysis and Data Mining. 5 (5): 363–387. doi:10.1002/sam.11161. S2CID 6724536.
Knorr, E. M.; Ng, R. T.; Tucakov, V. (2000). "Distance-based outliers: Algorithms and applications". The VLDB Journal the International Journal on Very Large Data Bases. 8 (3–4): 237. doi:10.1007/s007780050006. S2CID 11707259.
Schubert, E.; Zimek, A.; Kriegel, H. -P. (2012). "Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection". Data Mining and Knowledge Discovery. 28: 190–237. doi:10.1007/s10618-012-0300-z. S2CID 19036098.
Karch, Julian D. (2023). "Outliers may not be automatically removed". Journal of Experimental Psychology: General. 152 (6): 1735–1753. doi:10.1037/xge0001357. hdl:1887/4103722. PMID 37104797. S2CID 258376426.
Jaulin, L. (2010). "Probabilistic set-membership approach for robust regression" (PDF). Journal of Statistical Theory and Practice. 4: 155–167. doi:10.1080/15598608.2010.10411978. S2CID 16500768.

springer.com (Global: 182^nd place; English: 172^nd place)

link.springer.com

Smith, M.R.; Martinez, T.; Giraud-Carrier, C. (2014). "An Instance Level Analysis of Data Complexity". Machine Learning, 95(2): 225-256.

web.archive.org (Global: 1^st place; English: 1^st place)

Ripley, Brian D. 2004. Robust statistics Archived 2012-10-21 at the Wayback Machine
Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6) e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-11.
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4. Archived from the original (PDF) on 2015-09-23. Retrieved 2015-08-28.

wolfram.com (Global: 1,091^st place; English: 1,169^th place)

mathworld.wolfram.com

Weisstein, Eric W. Cauchy Distribution. From MathWorld--A Wolfram Web Resource

worldcat.org (Global: 4^th place; English: 4^th place)

search.worldcat.org

Zimek, Arthur; Filzmoser, Peter (2018). "There and back again: Outlier detection between statistical reasoning and data mining algorithms" (PDF). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6) e1280. doi:10.1002/widm.1280. ISSN 1942-4787. S2CID 53305944. Archived from the original (PDF) on 2021-11-14. Retrieved 2019-12-11.
Tukey, John W (1977). Exploratory Data Analysis. Addison-Wesley. ISBN 978-0-201-07616-5. OCLC 3058187.

Outlier (English Wikipedia)

adsabs.harvard.edu (Global: 14th place; English: 9th place)

articles.adsabs.harvard.edu

archive.org (Global: 6th place; English: 6th place)

books.google.com (Global: 3rd place; English: 3rd place)

doi.org (Global: 2nd place; English: 2nd place)

ensta-bretagne.fr (Global: low place; English: low place)

hdl.handle.net (Global: 66th place; English: 34th place)

jstor.org (Global: 23rd place; English: 15th place)

lmu.de (Global: low place; English: low place)

dbs.ifi.lmu.de

nih.gov (Global: 5th place; English: 5th place)

pubmed.ncbi.nlm.nih.gov

noaa.gov (Global: 111th place; English: 78th place)

docs.lib.noaa.gov

nrc.gov (Global: 5,536th place; English: 3,469th place)

ox.ac.uk (Global: 541st place; English: 356th place)

stats.ox.ac.uk

projecteuclid.org (Global: 4,781st place; English: 3,117th place)

psyarxiv.com (Global: low place; English: low place)

qualitydigest.com (Global: low place; English: low place)

sdu.dk (Global: low place; English: low place)

findresearcher.sdu.dk

semanticscholar.org (Global: 15th place; English: 8th place)

api.semanticscholar.org

springer.com (Global: 182nd place; English: 172nd place)

link.springer.com

web.archive.org (Global: 1st place; English: 1st place)

wolfram.com (Global: 1,091st place; English: 1,169th place)

mathworld.wolfram.com

worldcat.org (Global: 4th place; English: 4th place)

search.worldcat.org

adsabs.harvard.edu (Global: 14^th place; English: 9^th place)

archive.org (Global: 6^th place; English: 6^th place)

books.google.com (Global: 3^rd place; English: 3^rd place)

doi.org (Global: 2^nd place; English: 2^nd place)

hdl.handle.net (Global: 66^th place; English: 34^th place)

jstor.org (Global: 23^rd place; English: 15^th place)

nih.gov (Global: 5^th place; English: 5^th place)

noaa.gov (Global: 111^th place; English: 78^th place)

nrc.gov (Global: 5,536^th place; English: 3,469^th place)

ox.ac.uk (Global: 541^st place; English: 356^th place)

projecteuclid.org (Global: 4,781^st place; English: 3,117^th place)

semanticscholar.org (Global: 15^th place; English: 8^th place)

springer.com (Global: 182^nd place; English: 172^nd place)

web.archive.org (Global: 1^st place; English: 1^st place)

wolfram.com (Global: 1,091^st place; English: 1,169^th place)

worldcat.org (Global: 4^th place; English: 4^th place)