Fast statistical regression in presence of a dominant independent variable

Bivariate statistical regression is a statisti- cal tool that allows performing regression on a multi- variate data set under the hypothesis that one of the independent variables is dominant. Statistical regres- sion is profitable when the amount of available data is enough to explain the relevant statistical features of the phenomenon underlying the data. The present pa- per 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 avail- able data sets. A key point of the implementation pro- posed in the present paper is that it is based on purely numerical-algebraic operations, which guarantee a com- putationally advantageous way of implementing neu- ral systems. A number of numerical experiments, per- formed on real-world data sets, provide some insights into the behaviour of the devised neural-system-based statistical regression method and its limitations.