Abstract
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.
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Fiori, S. (2011). Statistical Nonparametric Bivariate Isotonic Regression by Look-Up-Table-Based Neural Networks. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_41
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DOI: https://doi.org/10.1007/978-3-642-24965-5_41
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