Summary
We show that MDS and related methods provide techniques and solutions to a wide field of topics in statistics, classification and data analysis: distance—based regression; representation of Hoeffding’s extremal correlations; examining a tree; representing parametric estimable functions in MANOVA; distance—based discrimination and classification; representation of a continuous random variable.
Keywords
- Multidimensional Scaling
- Maximum Correlation
- Canonical Variate Analysis
- Ultrametric Tree
- Classic Linear Discriminant
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
The first author thanks H. H. Bock and W. Gaul for their kind invitation to attend the 18th Annual Conference, Gesellschaft für Klassifikation e.V. in Oldenburg. We also thank M. Greenacre for useful comments. Work supported in part by CGYCIT grant PB93-0784
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Cuadras, C.M., Fortiana, J., Oliva, F. (1996). Representation of Statistical Structures, Classification and Prediction Using Multidimensional Scaling. In: Gaul, W., Pfeifer, D. (eds) From Data to Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79999-0_2
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DOI: https://doi.org/10.1007/978-3-642-79999-0_2
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