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Volume 50 Number 2 pp. 117-126
Andrzej Kornacki 1
1Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Akademicka 13, 20-950Lublin, Poland
Detection of outlying observations using the Akaike information criterion
Summary
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.
Keywords: outliers, entropy, Akaike information criterion, Dixon test, Grubbs test
DOI: 10.2478/bile-2013-0022
For citation:
| MLA | Kornacki, Andrzej. "Detection of outlying observations using the Akaike information criterion." Biometrical Letters 50.2 (2013): 117-126. DOI: 10.2478/bile-2013-0022 |
| APA | Kornacki, A. (2013). Detection of outlying observations using the Akaike information criterion. Biometrical Letters 50(2), 117-126 DOI: 10.2478/bile-2013-0022 |
| ISO 690 | KORNACKI, Andrzej. Detection of outlying observations using the Akaike information criterion. Biometrical Letters, 2013, 50.2: 117-126. DOI: 10.2478/bile-2013-0022 |