Abstract
Most multivariate tests are based on the hypothesis of multinormality. But often this hypothesis fails, or we have variables that are non quantitative. On the other hand we can deal with a large number of variables. Defining probabilistic models with mixed data is not easy. However, it is always possible to define a measure of distance between two observations. We prove that the use of distances can provide alternative tests for comparing several populations when the data are of general type. This approach is illustrated with three real data examples. We also define and study a measure of association between two data sets and make a Bayesian extension of the so-called distance-based discriminant rule.
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Cuadras, C.M. (2008). Distance Based Association and Multi-Sample Tests for General Multivariate Data. In: Advances in Mathematical and Statistical Modeling. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4626-4_5
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DOI: https://doi.org/10.1007/978-0-8176-4626-4_5
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