Abstract
This article proposesan easy to implement cluster-based method for identifying groups of nonhom ogeneous means. The method overcomes the common problem of the classical multiple-comparison methods that lead to the construction of groups that often have substantial overlap. In addition, it solves the problem of other cluster-based methods that do not have a known level of significance and are not easy to apply. The new procedure is compared by simulation with a set of classical multiple-comparison methods and a cluster-based one. Results show that the new procedure compares quite favorably with those included in this article.
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References
Bautista, M. G., Smith, D. W., and Steiner, R. L. (1997), «A Cluster-Based Approach to Means Separation,” Journal of Agricultural, Biological, and Environmental Statistics, 2, 179–197.
Calinski, T., and Corsten, L. C. A. (1985), “Clustering Means in ANOVA by Simultaneous Testing,” Biometrics, 41, 39–48.
Carmer, S. G., and Lin, W. T. (1983), “Type 1 Error Rates for Divisive Clustering Methods for Grouping Means in Analysis of Variance,” Communications in Statistics Simulation and Computation, Series B, 12, 451–466.
Carmer, S. G., and Swanson, M. R. (1973), “An Evaluation of Ten Pairwise Multiple Comparison Procedures by Monte Carlo Methods,” Journal of the American Statistical Association, 68, 66–74.
Cox, D. R., and Spjtvoll, E. (1982), “On Partitioning Means Into Groups,” Scandinavian Journal of Statistics, 9, 147–152.
Gates, C. E., and Bilbro, J. D. (1978), “Illustration of Cluster Analysis Method for Means Separation,” Agronomy Journal, 70, 462–465.
Jolliffe, I. T. (1975), “Cluster Analysis as a Multiple Comparíson Method,” Applied Statistics, Proceedings of Conference at Dalhousie University, Halifax, 159–168.
Jolliffe, I. T., Allen, O. B., and Christie, B. R. (1989), “Comparison of Variety Means Using Cluster Analysis and Dendrograms,” Experimental Agriculture, 25, 259–269.
Knuth, D. E. (1981), The Art of Computer Programming (Vol. 2, 2nd ed.), Seminumerical Algorithms, Reading, PA: Addison-Wesley.
Press, W. H., Flannery, P., Teukolsky, S. A., and Vetterling, W. T. (1986), Numerical Recipes, Cambridge: Cambridge University Press.
Scott, A. J., and Knott, M. (1974), “A Cluster Analysis Method for Grouping Means in the Analysis of Variance,” Biometrics, 30, 507–512.
Shaffer, J. P. (1981), “Complexity: An Interpretability Criterion for Multiple Comparisons,” Journal of the American Statistical Association, 76, 395–401.
Spath, H. (1980), Cluster Analysis Algorithms, New York: Wiley.
Tasaki, T., Yoden, A., and Goto, M. (1987), “Graphical Data Analysis in Comparative Experimental Studies,” Computational Statistics & Data Analysis, 5, 113–125.
Willavize, S. A., Carmer, S. G., and Walker, W. M. (1980), “Evaluation of Cluster Analysis for Comparing Treatment Means,” Agronomy Journal, 72, 317–320.
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Di Rienzo, J.A., Guzman, A.W. & Casanoves, F. A multiple-comparisons method based on the distribution of the root node distance of a binary tree. JABES 7, 129–142 (2002). https://doi.org/10.1198/10857110260141193
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DOI: https://doi.org/10.1198/10857110260141193