Outlier (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Outlier" in English language version.

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archive.org

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

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

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, CiteSeerX 10.1.1.109.1943, 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. CiteSeerX 10.1.1.43.1842. 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 1581132174.
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.
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 7 December 2024).{{cite journal}}: CS1 maint: DOI inactive as of December 2024 (link)

ensta-bretagne.fr

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.

handle.net

hdl.handle.net

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.

harvard.edu

articles.adsabs.harvard.edu

Benjamin Peirce, "Criterion for the Rejection of Doubtful Observations", Astronomical Journal II 45 (1852) and Errata to the original paper.

jstor.org

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

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.

nih.gov

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

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

E178: Standard Practice for Dealing With Outlying Observations

ox.ac.uk

stats.ox.ac.uk

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

projecteuclid.org

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.

psu.edu

citeseerx.ist.psu.edu

Hodge, Victoria J.; Austin, Jim (2004), "A Survey of Outlier Detection Methodologies", Artificial Intelligence Review, 22 (2): 85–126, CiteSeerX 10.1.1.109.1943, doi:10.1023/B:AIRE.0000045502.10941.a9, S2CID 3330313
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. CiteSeerX 10.1.1.43.1842. doi:10.1007/s007780050006. S2CID 11707259.

psyarxiv.com

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.

sdu.dk

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

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, CiteSeerX 10.1.1.109.1943, 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. CiteSeerX 10.1.1.43.1842. 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

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

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.

wolfram.com

mathworld.wolfram.com

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

worldcat.org

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.