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Detection of outlying observations using the Akaike information criterion

100%
Kornacki A.
Biometrical Letters
|
2013
|
tom 50
|
nr 2
117-126
EN
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.
2
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The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments

63%
Kornacki A., Bochniak A.
Biometrical Letters
|
2015
|
tom 52
|
nr 2
75-84
EN
In this study the Akaike information criterion for detecting outliers in a log-normal distribution is used. Theoretical results were applied to the identification of atypical varietal trials. This is an alternative to the tolerance interval method. Detection of outliers with the help of the Akaike information criterion represents an alternative to the method of testing hypotheses. This approach does not depend on the level of significance adopted by the investigator. It also does not lead to the masking effect of outliers.
3
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Sample size determination in the Mann–Whitney test

51%
Kornacki A., Bochniak A., Kubik-Komar A.
Biometrical Letters
|
2017
|
tom 54
|
nr 2
175-186
EN
This paper discusses the problem of determining the number of observations necessary to apply the nonparametric Mann-Whitney test. We describe the method given by Noether (1987) for determining a sample size which guarantees that the Mann-Whitney test at a given significance level α has a predetermined power 1-β. The presented theory is tested by calculating the empirical power in computer simulations. The paper also raises the issue of the method of rounding the determined sample size to an even number when the sample is divided into two equinumerous subsamples.
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