Abstract
We establish two-way analysis of variance (ANOVA) for interval-valued data. Each observation is assumed to be a compact convex interval, and the two-way ANOVA determines whether to reject null hypotheses about the effects of two factors on the observed intervals. The Minkowski support function is used to obtain a metric for intervals and to transform them to Hilbert-space-valued functions. We derive test statistics that are appropriate for testing the null hypotheses, and we develop a bootstrap scheme for approximating the p-values of the observed test statistics.
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Nakama, T., Colubi, A., Lubiano, M.A. (2010). Two-Way Analysis of Variance for Interval-Valued Data. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_59
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DOI: https://doi.org/10.1007/978-3-642-14746-3_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14745-6
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