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2015 | 52 | 2 | 75-84
Tytuł artykułu

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

Treść / Zawartość
Warianty tytułu
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.
Wydawca
Czasopismo
Rocznik
Tom
52
Numer
2
Strony
75-84
Opis fizyczny
Daty
wydano
2015-12-01
online
2015-12-12
Twórcy
  • Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Akademicka 15, 20-950 Lublin, Poland, andrzej.kornacki@up.lublin.pl
  • 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]
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.doi-10_1515_bile-2015-0007
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