The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments

• Autorzy: Andrzej Kornacki , Andrzej Bochniak
• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

outliers; log-normal distribution; atypical variety trials; hypothesis testing; masking of outliers; wheat; entropy

• Wydawca: De Gruyter Open
• Czasopismo: Biometrical Letters
• Rocznik: 2015
• Tom: 52
• Numer: 2
• Strony: 75-84

Daty

wydano

2015-12-01

online

2015-12-12

Twórcy

autor

Andrzej Kornacki

• Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Akademicka 15, 20-950 Lublin, Poland

autor

Andrzej Bochniak

• Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Akademicka 15, 20-950 Lublin, Poland

Bibliografia

• 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.
• 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. (1994): Outliers in Statistical Data. John Wiley & Sons.
• 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.
• Grubbs F.E. (1960): Sample criteria for testing outlying observations. Ann. Math. Statist. 21: 27-58.
• 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.
• Sakamoto Y., Ishiguro M., Kitagawa G. (1986): Akaike Information Criterion Statistics. Tokyo Reidel Publishing Company.
• Srivastava M.S., Von Rosen D. (1998): Outliers in Multivariate Regression Models. J. Mult. Anal. 65: 195-208.
• Stefansky W. (1972): Rejecting outliers in factorial designs. Technometrics 14: 469-479. [Crossref]

Identyfikatory

DOI

10.1515/bile-2015-0007