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Fast statistical regression in presence of a dominant independent variable

  • ICONIP 2011
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Abstract

Bivariate statistical regression is a statistical tool that allows performing regression on a multivariate data set under the hypothesis that one of the independent variables is dominant. Statistical regression is profitable when the amount of available data is enough to explain the relevant statistical features of the phenomenon underlying the data. The present paper suggests a fast statistical regression method based on a neural system that is able to match its input–output statistic to the marginal statistic of the available data sets. A key point of the implementation proposed in the present paper is that it is based on purely numerical-algebraic operations, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on real-world data sets, provide some insights into the behavior of the devised neural-system-based statistical regression method and its limitations.

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Acknowledgments

The present paper is an extended version of the conference paper [10]. The author wishes to thank Andrew Leung for the invitation to submit the present extended version to the special issue of Neural Computation and Applications dedicated to the ICONIP’2011 conference.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche, 60131, Ancona, Italy

    Simone Fiori

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  1. Simone Fiori
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Fiori, S. Fast statistical regression in presence of a dominant independent variable. Neural Comput & Applic 22, 1367–1378 (2013). https://doi.org/10.1007/s00521-012-0958-6

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