Detection of outlying observations using the Akaike information criterion

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.