Abstract
This final chapter concerns some discussion to emphasize the role of nonparametric combination as a flexible methodology for solving complex problems. These complex testing problems are not adequately taken into consideration in the standard literature. This is in spite of the fact that they are very frequently encountered in a great variety of practical applications. These problems emphasize the versatility and effectiveness of the nonparametric combination methodology. It should also be stressed that since permutation tests are conditional with respect to a set of sufficient statistics, the nonparametric combination, in very mild conditions, frees the researcher from the necessity to model the dependence relations among responses. Some future research guidelines using permutation tests are also presented.
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References
H.P. Piepho, Permutation tests for the correlation among genetic distances and measures of heterosis. Theor. Appl. Genet. 111, 95–99 (2005)
F. Pesarin, L. Salmaso, Permutation Tests for Complex Data: Theory, Applications and Software (Wiley, Chichester, 2010)
Acknowledgments
Authors wish to thank the University of Padova (CPDA092350/09) and the Italian Ministry for University and Research (2008WKHJPK/002) for providing the financial support for this research.
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© 2011 Luigi Salmaso
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Salmaso, L., Arboretti, R., Corain, L., Mazzaro, D. (2011). Conclusions. In: Permutation Testing for Isotonic Inference on Association Studies in Genetics. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20584-2_7
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DOI: https://doi.org/10.1007/978-3-642-20584-2_7
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