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Journal of Applied Mathematics
Volume 2015, Article ID 708948, 9 pages
http://dx.doi.org/10.1155/2015/708948
Research Article

Data Transformation Technique to Improve the Outlier Detection Power of Grubbs’ Test for Data Expected to Follow Linear Relation

1Group Bio-Process Analysis Technology, Technische Universität München, Weihenstephaner Steig 20, 85354 Freising, Germany
2Institut für Landtechnik und Tierhaltung, Vöttinger Straße 36, 85354 Freising, Germany
3Computer Unit, Faculty of Agriculture, University of Ruhuna, Mapalana, 81100 Kamburupitiya, Sri Lanka

Received 9 September 2014; Revised 9 December 2014; Accepted 10 December 2014

Academic Editor: Carlos Conca

Copyright © 2015 K. K. L. B. Adikaram et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. E. Grubbs, “Sample criteria for testing outlying observations,” The Annals of Mathematical Statistics, vol. 21, no. 1, pp. 27–58, 1950. View at Publisher · View at Google Scholar · View at MathSciNet
  2. F. E. Grubbs, “Procedures for detecting outlying observations in samples,” Technometrics, vol. 11, no. 1, pp. 1–21, 1969. View at Publisher · View at Google Scholar
  3. F. E. Grubbs and G. Beck, “Extension of sample sizes and percentage points for significance tests of outlying observations,” Technometrics, vol. 14, pp. 847–854, 1972. View at Google Scholar · View at MathSciNet
  4. M. Thompson and P. J. Lowthian, Notes on Statistics and Data Quality for Analytical Chemists, Imperial College Press, 2011.
  5. S. Geisser, “Influential observations, diagnostics and discovery tests,” Journal of Applied Statistics, vol. 14, no. 2, pp. 133–142, 1987. View at Publisher · View at Google Scholar
  6. W.-K. Fung, “A statistical-test-complemented graphical method for detecting multiple outliers in two-way tables,” Journal of Applied Statistics, vol. 18, no. 2, pp. 265–274, 1991. View at Publisher · View at Google Scholar
  7. B. M. Colosimo, R. Pan, and E. del Castillo, “A sequential Markov chain Monte Carlo approach to set-up adjustment of a process over a set of lots,” Journal of Applied Statistics, vol. 31, no. 5, pp. 499–520, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. M. K. Solak, “Detection of multiple outliers in univariate data sets,” Paper SP06-2009, Schering, 2009. View at Google Scholar
  9. R. B. Jain, “A recursive version of Grubbs' test for detecting multiple outliers in environmental and chemical data,” Clinical Biochemistry, vol. 43, no. 12, pp. 1030–1033, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  10. B. Rosner, “On the detection of many outliers,” Technometrics, vol. 17, pp. 221–227, 1975. View at Google Scholar · View at MathSciNet
  11. B. Rosner, “Percentage points for a generalized ESD many-outlier procedure,” Technometrics, vol. 25, no. 2, pp. 165–172, 1983. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Brant, “Comparing classical and resistant outlier rules,” Journal of the American Statistical Association, vol. 85, no. 412, pp. 1083–1090, 1990. View at Publisher · View at Google Scholar
  13. L. Xu, P. Zhang, J. Xu, S. Wu, G. Han, and D. Xu, “Conflict analysis of multi-source SST distribution,” in High Performance Computing and Applications, W. Zhang, Z. Chen, C. C. Douglas, and W. Tong, Eds., pp. 479–484, Springer, Berlin, Germany, 2010. View at Google Scholar
  14. M. S. Srivastava, “Effect of equicorrelation in detecting a spurious observation,” The Canadian Journal of Statistics, vol. 8, no. 2, pp. 249–251, 1980. View at Publisher · View at Google Scholar · View at MathSciNet
  15. D. M. Young, R. Pavur, and V. R. Marco, “On the effect of correlation and unequal variances in detecting a spurious observation,” The Canadian Journal of Statistics, vol. 17, no. 1, pp. 103–105, 1989. View at Publisher · View at Google Scholar · View at MathSciNet
  16. J. K. Baksalary and S. Puntanen, “A complete solution to the problem of robustness of Grubbs's test,” The Canadian Journal of Statistics, vol. 18, no. 3, pp. 285–287, 1990. View at Publisher · View at Google Scholar · View at MathSciNet
  17. O. H. J. Christie and K. H. Alfsen, “Data transformation as a means to obtain reliable consensus values for reference materials,” Geostandards and Geoanalytical Research, vol. 1, no. 1, pp. 47–49, 1977. View at Publisher · View at Google Scholar