Detection of outlying observations using the Akaike information criterion

Andrzej Kornacki

Biometrical Letters (2013)

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

Abstract

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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.

How to cite

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Andrzej 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

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  2. 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. 
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