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.
@article{bwmeta1.element.doi-10_1515_bile-2015-0007,
author = {Andrzej Kornacki and Andrzej Bochniak},
title = {The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments},
journal = {Biometrical Letters},
volume = {52},
year = {2015},
pages = {75-84},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.doi-10_1515_bile-2015-0007}
}
Andrzej Kornacki; Andrzej Bochniak. The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments. Biometrical Letters, Tome 52 (2015) pp. 75-84. http://gdmltest.u-ga.fr/item/bwmeta1.element.doi-10_1515_bile-2015-0007/
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; Akademia Kiado: 267-281. | Zbl 0283.62006
Akaike H. (1977): On entropy maximization principle. Proc Symposium on Applications of Statistics. ed. P.R. Krishnaiah. Amsterdam: North Holland: 27-47. | Zbl 0388.62008
Barnett V., Lewis T. (1994): Outliers in Statistical Data. John Wiley & Sons. | Zbl 0801.62001
Breuning M., Kriegel H.P, Sander J. (2000): LOF: Identifying Density-Based Local. Proceedings of the ACM SIGMOND Conference: 93-104.
David H.A., Nagaraja H.N. (2003): Order Statistics. Wiley Series in Probability and Statistics.
Ferguson T.S. (1961): On the rejection of outliers. Proc. Fourth Berkeley Symposium Math. Statist. Prob.1: 253-287. | Zbl 0129.32702
Grubbs F.E. (1960): Sample criteria for testing outlying observations. Ann. Math. Statist. 21: 27-58. | Zbl 0036.21003
Grubbs F.E. (1969): Procedures for detecting outlying observations in samples. Technometrics 11: 1-21.[Crossref]
Krzyśko. M. (2004): Mathematical Statistics. Poznań, Wydawnictwo Naukowe UAM (in Polish).
Limpert. E., Stahel W., Abbt M. (2001): Log-normal distribution across the sciences: Kees and Clues. Bioscience. 51(5): 341-352.[Crossref]
Ohanowicz T., Pilarczyk W. (1985): Precision experiments with potato and detection of unusual experiments. XV Colloquium Metodologiczne z Agrobiometrii: 106-115 (in Polish).
Pilarczyk W. (1988): The effectiveness of varietal trials with cereals and detection of untypical experiments. Biuletyn Oceny Odmian. 13: 115-123 (in Polish).
Ramaswamy S., Rastogi R., Shim K. (2000): Efficient algorithms for mining outliers from large data sets. Proceedings of the ACM SIGMOND Conference: 427-438.
Rousseeuw P., Leroy A. (2003): Robust Regression and Outlier Detection. John Wiley & Sons. | Zbl 0711.62030
Sakamoto Y., Ishiguro M., Kitagawa G. (1986): Akaike Information Criterion Statistics. Tokyo Reidel Publishing Company. | Zbl 0608.62006
Srivastava M.S., Von Rosen D. (1998): Outliers in Multivariate Regression Models. J. Mult. Anal. 65: 195-208. | Zbl 1127.62376
Stefansky W. (1972): Rejecting outliers in factorial designs. Technometrics 14: 469-479. [Crossref] | Zbl 0284.62053