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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Statistical Nonparametric Bivariate Isotonic Regression by Look-Up-Table-Based Neural Networks
verfasst von : Simone Fiori
Erschienen in: Neural Information Processing
Verlag: Springer Berlin Heidelberg
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Bivariate regression allows inferring a model underlying two data-sets. We consider the case of regression from possibly incomplete data sets, namely the case that data in the two sets do not necessarily correspond in size and might come unmatched/unpaired. The paper proposes to tackle the problem of bivariate regression through a non-parametric neural-learning method that is able to match the statistics of the available data sets. The devised neural algorithm is based on a look-up-table representation of the involved functions. A numerical experiment, performed on a real-world data set, serves to illustrate the features of the proposed statistical regression procedure.