Abstract
A least squares algorithm for fitting additive trees to proximity data is described. The algorithm uses a penalty function to enforce the four point condition on the estimated path length distances. The algorithm is evaluated in a small Monte Carlo study. Finally, an illustrative application is presented.
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The author is “Aspirant” of the Belgian “Nationaal Fonds voor Wetenschappelijk Onderzoek”. The author is indebted to Professor J. Hoste for providing computer facilities at the Institute of Nuclear Sciences at Ghent.
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De Soete, G. A least squares algorithm for fitting additive trees to proximity data. Psychometrika 48, 621–626 (1983). https://doi.org/10.1007/BF02293884
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DOI: https://doi.org/10.1007/BF02293884