# Detection of outlying observations using the Akaike information criterion

Biometrical Letters (2013)

- Volume: 50, Issue: 2, page 117-126
- ISSN: 1896-3811

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topAndrzej Kornacki. "Detection of outlying observations using the Akaike information criterion." Biometrical Letters 50.2 (2013): 117-126. <http://eudml.org/doc/268816>.

@article{AndrzejKornacki2013,

abstract = {For the detection of outliers (observations which are seemingly different from the others) the method of testing hypotheses is most often used. This approach, however, depends on the level of significance adopted by the investigator. Moreover, it can lead to the undesirable effect of “masking” of the outliers. This paper presents an alternative method of outlier detection based on the Akaike information criterion. The theory presented is applied to analysis of the results of beet leaf mass determination.},

author = {Andrzej Kornacki},

journal = {Biometrical Letters},

keywords = {outliers; entropy; Akaike information criterion; Dixon test; Grubbs test},

language = {eng},

number = {2},

pages = {117-126},

title = {Detection of outlying observations using the Akaike information criterion},

url = {http://eudml.org/doc/268816},

volume = {50},

year = {2013},

}

TY - JOUR

AU - Andrzej Kornacki

TI - Detection of outlying observations using the Akaike information criterion

JO - Biometrical Letters

PY - 2013

VL - 50

IS - 2

SP - 117

EP - 126

AB - For the detection of outliers (observations which are seemingly different from the others) the method of testing hypotheses is most often used. This approach, however, depends on the level of significance adopted by the investigator. Moreover, it can lead to the undesirable effect of “masking” of the outliers. This paper presents an alternative method of outlier detection based on the Akaike information criterion. The theory presented is applied to analysis of the results of beet leaf mass determination.

LA - eng

KW - outliers; entropy; Akaike information criterion; Dixon test; Grubbs test

UR - http://eudml.org/doc/268816

ER -

## References

top- Akaike H. (1973): Information theory and an extension of the maximum likelihood principle. 2nd International Symposium on Information Theory, eds. B.N. Petrv and F. Csaki, Budapest; Akademiai Kiado: 267-281. Zbl0283.62006
- Akaike H. (1977): On entropy maximization principle. Proc Symposium on Applications of Statistics, ed. P.R. Krishnaiah, Amsterdam: North Holland: 27-47. Barnett V., Lewis T. (1993): Outliers in Statistical Data. John Wiley & Sons.
- Breuning M., Kriegel H.P, Sander J. (2000): LOF: Identifying Density-Based Local Outliers. In: Proceedings of the ACM SIGMOND Conference: 93-104.
- David H.A. (1979): Pariadkowyje statistiki. Mockba Nauka.
- Dzida K., Jarosz Z., Michalojć Z. (2011): The effect of diversified potassium fertilization on the field and chemical composition on Beta Vulgaris L. Acta Sci. Pol. Hortus. Cultus 10(40): 263-274.
- Ellenberg J.H. (1976): Testing for a single outlier from a general linear regression. Biometrics 32: 637-645.[PubMed][Crossref] Zbl0338.62039
- Ferguson T.S. (1961): On the rejection of outliers. In Proc. Fourth Berkeley Symposium Math. Statist. Prob.1: 253-287. Zbl0129.32702
- Galpin J.S., Hawkins D.M. (1981): Rejection of a single outlier in two or three-way layouts. Technometrics 23: 65-70. Zbl0465.62070
- Grubbs F.E. (1950): Sample criteria for testing outlying observations. Ann. Math. Statist. 21: 27-58. Zbl0036.21003
- Grubbs F.E. (1969): Procedures for detecting outlying observations in samples. Technometrics 11: 1-21.[Crossref]
- Joshi P.C. (1972): Some slippage tests of mean for a single outlier in linear regression. Biometrika 59: 109-120.[Crossref] Zbl0234.62028
- Karlin S., Traux D. (1960): Slippage problems. Ann. Math. Statist 31: 296-324.[Crossref] Zbl0131.35602
- Pan J.X., Fang K.T. (1995): Multiple outlier detection in growth curve model with unstructured covariance matrix. Ann. Inst. Statist. Math. 47: 137-153. Zbl0822.62045
- Ramaswamy S., Rastogi R., Shim K. (2000): Efficient algorithms for mining outliers from large data sets. In: Proceedings of the ACM SIGMOND Conference on Management of data. Dallas: 427-438.
- Rosseuw P., Leroy A. (2000): Robust Regression and Outlier Detection. John Wiley & Sons.
- Sakamoto Y., Ishigura M. (1986): Akaike Information Criterion Statistics. Tokyo Reidel Publishing Company.
- Schwager S.J., Margolin B.H. (1982): Detection of multivariate normal outliers. Ann. Statist. 10: 943-954. Zbl0497.62046
- Srivastava M.S., Von Rosen D. (1998): Outliers in Multivariate Regression Models. J. Mult. Anal. 65: 195-208. Zbl1127.62376
- Stefansky W. (1972): Rejecting outliers in factorial designs. Technometrics 14: 469-479 [Crossref] Zbl0284.62053
- Tietjen G.L., Moore R.H. (1972): Some Grubbs-type statistics for the detection of several outliers. Technometrics 14: 583-597.[Crossref]
- Wilks S.S. (1963): Multivariate statistical outliers. Sankhya A. 25: 406-427. Zbl0128.13401

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