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

Andrzej Kornacki; Andrzej Bochniak

Biometrical Letters (2015)

  • Volume: 52, Issue: 2, page 75-84
  • ISSN: 1896-3811

Abstract

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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.

How to cite

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Andrzej Kornacki, and Andrzej Bochniak. "The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments." Biometrical Letters 52.2 (2015): 75-84. <http://eudml.org/doc/276002>.

@article{AndrzejKornacki2015,
abstract = {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.},
author = {Andrzej Kornacki, Andrzej Bochniak},
journal = {Biometrical Letters},
keywords = {outliers; log-normal distribution; atypical variety trials; hypothesis testing; masking of outliers; wheat; entropy},
language = {eng},
number = {2},
pages = {75-84},
title = {The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments},
url = {http://eudml.org/doc/276002},
volume = {52},
year = {2015},
}

TY - JOUR
AU - Andrzej Kornacki
AU - Andrzej Bochniak
TI - The use of outlier detection methods in the log-normal distribution for the identification of atypical varietal experiments
JO - Biometrical Letters
PY - 2015
VL - 52
IS - 2
SP - 75
EP - 84
AB - 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.
LA - eng
KW - outliers; log-normal distribution; atypical variety trials; hypothesis testing; masking of outliers; wheat; entropy
UR - http://eudml.org/doc/276002
ER -

References

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  1. 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. Zbl0283.62006
  2. Akaike H. (1977): On entropy maximization principle. Proc Symposium on Applications of Statistics. ed. P.R. Krishnaiah. Amsterdam: North Holland: 27-47. Zbl0388.62008
  3. Barnett V., Lewis T. (1994): Outliers in Statistical Data. John Wiley & Sons. Zbl0801.62001
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  11. Ohanowicz T., Pilarczyk W. (1985): Precision experiments with potato and detection of unusual experiments. XV Colloquium Metodologiczne z Agrobiometrii: 106-115 (in Polish). 
  12. Pilarczyk W. (1988): The effectiveness of varietal trials with cereals and detection of untypical experiments. Biuletyn Oceny Odmian. 13: 115-123 (in Polish). 
  13. Ramaswamy S., Rastogi R., Shim K. (2000): Efficient algorithms for mining outliers from large data sets. Proceedings of the ACM SIGMOND Conference: 427-438. 
  14. Rousseeuw P., Leroy A. (2003): Robust Regression and Outlier Detection. John Wiley & Sons. Zbl0711.62030
  15. Sakamoto Y., Ishiguro M., Kitagawa G. (1986): Akaike Information Criterion Statistics. Tokyo Reidel Publishing Company. Zbl0608.62006
  16. Srivastava M.S., Von Rosen D. (1998): Outliers in Multivariate Regression Models. J. Mult. Anal. 65: 195-208. Zbl1127.62376
  17. Stefansky W. (1972): Rejecting outliers in factorial designs. Technometrics 14: 469-479. [Crossref] Zbl0284.62053

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