Detection of outliers

Wiley Interdisciplinary Reviews: Computational Statistics

Volumn 1, Issue 1, 2009, Pages 57-70

  Hadi, Ali S ^a,b   Rahmatullah Imon, A H M ^c   Werner, Mark ^a  

^a The American University in Cairo   (Egypt)

^b Department of Molecular Biology and Genetics   (United States)

^c BALL STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LARGE DATASETS; MAHALANOBIS DISTANCES; PRINCIPAL COMPONENTS; PROJECTION PURSUITS;

EID: 77952419438     PISSN: 19395108     EISSN: 19390068     Source Type: Journal    
DOI: 10.1002/wics.6     Document Type: Review

References (77)

1. 38
2. Legendre AM. On the method of least squares. In Smith D ed. A Source Book in Mathematics. Dover Publications; 1805, 1959, 575-1569
3. Edgeworth FY. The choice of means. Philos Mag 1887, 24(5): 268-271.
4. Tukey JW. Exploratory Data Analysis. Reading, MA: Addison-Wesley; 1977.
5. Kafa dar K, Morris M. Data-based detection of potential terrorist attacks on airplanes, White paper. Available at: http://www.amstat.org/sections/sdns/ kafada.pdf, 2002.
6. Hadi AS, Simonoff JS. Procedures for the identification of multiple outliers in linear models. J Am Stat Assoc 1993, 88: 1264-1272.
7. Franses PH, Lucas A. Outlier detection in cointegration analysis. J. Bus Econ Stat 1998, 16(4): 459-468.
8. 8. Varbanov A. Bayesian approach to outlier detection in multivariate normal samples and linear models. Commun Stat Theory Methods 1998, 27(3): 547-557. (Pubitemid 128454909)
9. Munier S. Multiple outlier detection in logistic regression. Student 1999, 3: 117-126.
10. Tsay RS, Peña D, Pankratz A. Outliers in multivariate time series. Biometrika 2000, 87(4): 789-804.
11. Bianco AM, Ben MG, Martiínez EJ, Yohai VJ. Outlier detection in regression models with ARIMA errors using robust estimates. J Forecast 2001, 20: 565-579.
12. 12. Chung Y, Dey DK, Lee S. Applying the Savage-Dickey density ratio to default bayes factors, with an illustration to outlier detection, in random, effects models. Commun Stat Theory Methods 2001, 30(12): 2563-2582. (Pubitemid 34107490)
13. Kaiser R, Maravall A. Seasonal outliers in time series. Estadistica 2001, 53: 97-142.
14. Pierna JAF, Wahl F, de Noord OE, Massart DL. Methods for outlier detection in prediction. Chemometr Intell Lab Syst 2002, 63: 27-39.
15. McQuarrie A, Tsai C-L. Outlier detections in autoregressive models. J Comput Graph Stat 2003, 12(2): 450-471.
16. 16. Hardin J, Rocke DM. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. Comput Stat Data Anal 2004, 44: 625-638. (Pubitemid 37471079)
17. Mebane WR, Sekhon JS. Robust estimation and outlier detection for overdispersed multinomial models of count data. Am J Pol Sci 2004, 48(2): 392-411.
18. 18. Tolvi J. Genetic algorithms for outlier detection and variable selection in linear regression models. Soft Comput 2004, 8: 527-533. (Pubitemid 39168569)
19. 19. Battaglia F, Orfei L. Outlier detection and estimation in nonlinear time series. J Time Ser Anal 2005, 26(1): 107-121. (Pubitemid 40145151)
20. 20. Agyemang M, Barker K, Alhajj R. WCOND-Mine: Algorithm for detecting web content outliers from web documents. In: Proceedings of the 10th IEEE Symposium on Computers and Communications (ISCC 2005), Cartagena, Spain; 2005, 885-890. (Pubitemid 41548337)
21. 21. Shyu ML, Sarinnapakorn K, Kuruppu-Appuhamilage I, Chen SC, Chang, L, et al. Handling nominal features in anomaly intrusion detection problems. In: Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA 2005); 2005, 55-62. (Pubitemid 41500260)
22. 22. Takeuchi J, Yamanishi K. A unifying framework for detecting outliers and change points from time series. IEEE Trans Knowl Data Eng 2006, 18(4): 482-492. (Pubitemid 43327098)
23. Ruppert D, Simpson. DG. Comment on unmasking multivariate outliers and leverage points. J Am Stat Assoc 1990, 85: 644-646.
24. Friedman JH, Tukey JW. A projection pursuit algorithm for exploratory data analysis. IEEE Trans Comput 1974, 23(9): 881-889.
25. Huber PJ. Projection pursuit. Ann Stat 1985, 13(2): 435-475.
26. Stahel WA. Breakdown of Covariance Estimators, Research Report 31. Fachgruppe für Statistik, E.T.H. Zürich, 1981.
27. Donoho, DL. Breakdown Properties of Multivariate Location Estimators. PhD thesis, Harvard University, 1982.
28. Maronna RA, Yohai VJ. The behavior of the Stahel-Donoho robust multivariate estimator. J Am Stat Assoc 1995, 90(429): 330-341.
29. 29. Juan J, Prieto F. A subsampling method for the computation of multivariate estimators with high breakdown point. J Comput Graph Stat 1995, 4: 319-334. (Pubitemid 126019467)
30. Muruzàbal J. A note on the Stahel-Donoho estimator for outlier detection. Commun Stat Comput Simulat 1999, 28(1): 207-222.
31. Peña D, Prieto F. Multivariate outlier detection and robust covariance matrix estimation. Technometrics 2001, 43(3): 286-310.
32. Rocke DM, Woodruff DL. Identification of multivariate outliers. J Am Stat Assoc 1996, 91(435): 1047-1061.
33. Hardin J, Rocke DM. The distribution of robust distances. J Comput Graph Stat 2005, 14: 928-946.
34. Rousseeuw PJ. Multivariate estimation with high breakdown point. In: Grossmann W, Pflug G, Vincze I, Wertz W, eds. Mathematical Statistics and Applications, Vol B. Dordrecht: Reidel Publishing Company; 1985, 283-297.
35. Lopuhaä HP, Rousseeuw PJ. Breakdown points of affine equivariant estimators of multivariate location and covariance matrices. Ann Stat 1991, 19(1): 229-248.
36. Rousseeuw PJ, Van Driessen K. A fast algorithm for the minimum covariance determinant estimator. Technometrics 1999, 41(3): 212-223.
37. Rocke DM. Constructive statistics: estimators, algorithms, and asymptotics. Comput Sci Stat 1998, 30: 3-14.
38. 38. Billor N, Hadi AS, Velleman PF. BACON: Blocked adaptive computationally-efficient outlier nominators. Comput Stat Data Anal 2000, 34: 279-298. (Pubitemid 30761731)
39. Hadi AS. Identifying multiple outliers in multivariate data. J R Stat Soe Ser B 1992, 54: 761-771.
40. Hadi AS. A modification of a method for the detection of outliers in multivariate samples. J R Stat Soe Ser B 1994, 56: 393-396.
41. Maronna RA, Zamar RH. Robust estimates of location and dispersion for high-dimensional data sets. Technometrics 2002, 44(4): 307-317.
42. Locantore N, Marron JS, Simpson DG, Tripoli N, Zhang JT, et al. Robust principal component analysis for functional data. Test 1999, 8(1):173.
43. Huber PJ. Robust Statistics. New York: Wiley, 1981.
44. Hössjer O, Croux C. Generalizing univariate signed rank statistics for testing and estimating a multivariate location parameter. J Nonparametr Stat 1995, 4: 293-308.
45. Franklin S, Brodeur M. A practical application of a robust multivariate outlier detection method. In: Proceedings of The American Statistical Association: Section on Survey Research; 1997, 186-191.
46. Campbell NA. Robust procedures in multivariate analysis I: Robust covariance estimation. Appl Stat 1980, 29: 231-237.
47. 47. Caroni C. Outlier detection by robust principal component analysis. Commun Stat Simulat Comput 2000, 29(1): 139-151. (Pubitemid 33791448)
48. 48. Hubert M, Rousseeuw PJ, Branden KV. ROBPCA: a new approach to robust principal components analysis. Technometrics 2005, 47: 64-79. (Pubitemid 40213976)
49. Filzmoser P, Maronna RA, Werner M. Outlier identification in high dimensions. Comput Stat Data Anal 2008, 52: 1694-1711.
50. 50. Rocke D. M. Robustness properties of s-estimators of multivariate location and shape in high dimension. Ann Stat 1996, 24(3): 1327-1345. (Pubitemid 126131284)
51. Hodge VJ, Austin J. A survey of outlier detection, methodologies. Artif Intell Rev 2004, 22: 85-126.
52. Knorr E, Ng R, Tucakov V. Distance-based outliers: algorithms and applications. VLDB J 2000, 8: 237-253.
53. Ren. D, Rahal I, Perrizo W, Scott K. A vertical distancebased outlier detection method with local pruning. In: Proceedings of the 2004 ACM International Conference on Information and Knowledge Management (CIKM). New York: ACM Press; 2004, 279-284.
54. Ren D, Wang B, Perrizo W. RDF: A density-based outlier detection method using vertical data representation. In: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004). Washington, D. C.: IEEE; 2004, 503-506.
55. 55. Aggarwal CC, Yu P. An effective and efficient algorithm for high-dimensional outlier detection. The VLDB Journal 2005, 14: 211-221. (Pubitemid 40536894)
56. 56. Angiulli F, Pizzuti C. Outlier mining in large highdimensional data sets. IEEE Trans Knowl Data Eng 2005, 17(2): 203-215. (Pubitemid 40320179)
57. Angiulli F, Basta S, Pizzuti C. Distance-based detection and prediction of outliers. IEEE Trans Knowl Data Eng 2006,18(2): 145-160.
58. Ghoting A, Parthasarathy S, Otey ME. Fast mining of distance-based outliers in high dimensional datasets. Data Mining and Knowledge Discovery 2008, 16(3): 349-364.
59. Kollios G, Gunopulos D, Koudas N, Berchtold S. Efficient biased sampling for approximate clustering and outlier detection, in large data sets. IEEE Trans Knowl Data Eng 2003, 15(5): 1170-1187.
60. Breunig M, Kriegel HP, Ng R, Sander J. LOF: Identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. New York: ACM Press; 2000, 93-104.
61. Jin W, Tung AKH, Han J. Mining top-n local outliers in large databases. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '01). New York: ACM Press; 2001, 293-298.
62. Chiu ALM, Fu AWC. Enhancements on local outlier detection. In: Proceedings of the Seventh International Database Engineering and Applications Symposium (IDEAS '03). Hong Kong: IEEE; 2003, 298-307.
63. Hu T, Sung SY. Detecting pattern-based outliers. Pattern Recognit Lett 2003, 24: 3059-3068.
64. Papadimitriou S, Kitagawa H, Gibbons PB, Faloutsos C. LOCI: Fast outlier detection using the local correlation integral. In: Proceedings of the 19th International Conference on Data Engineering; 2003, 315-326.
65. Agyemang M, Ezeife CI. LSC-Mine: Algorithm for mining local outliers. In: Proceedings of the 15th Information Resource Management Association International Conference; 2004, 5-8.
66. Huang T, Qin X. Detecting outliers in spatial database. In: Proceedings of the Third International Conference on Image and Graphics (ICIG '04). IEEE; 2004, 556-559.
67. Chawla S, Sun P. SLOM: a new measure for local spatial, outliers. Knowl Inform Syst 2006, 9(4): 412-429.
68. Jin W, Tung AKH, Han J, Wang W (2006), Ranking outliers using symmetric neighborhood relationship. In: Advances in Knowledge Discovery and Data Mining, 10th Pacific-Asia Conference (PAKDD 2006), Lecture Notes in Computer Science, Vol.3918. Berlin: Springer 577-593. (Pubitemid 44019461)
69. Tang J, Chen Z, Fu AW, Cheung DW. Capabilities of outlier detection schemes in large datasets, framework and methodologies. Knowl Inform Syst 2007, 11(1):45-84.
70. Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA. Robust Statistics: The Approach Based on Influence Functions. New York: Wiley; 1986.
71. Hoaglin DC, Mosteller F, Tukey JW. Understanding Robust and Exploratory Data Analysis. New York: Wiley; 1983.
72. Jureckova J, Picek J. Robust Statistical Methods with R. Chapman Oc Hall / CRC; 2006.
73. Maronna RA, Martin DR, Yohai VJ. Robust statistics: Theory and Methods. New York: Wiley; 2006.
74. Maronna RA, Yohai VJ. Robust estimation of multivariate location and scatter. In: Kotz S, Read C, Banks D eds. Encyclopedia of Statistical Sciences. New York: Wiley; 1998, 589-596.
75. Rousseeuw PJ, Leroy AM. Robust Regression and Outliers Detection. New York: Wiley; 1987.
76. Staudte RG, Sheather SJ. Robust Estimation and Testing. New York: Wiley; 1990.
77. Wilcox RR. Introduction to Robust Estimation and Hypothesis Testing. San Diego, CA: Academic Press; 2005.