New methods in data analysis and economic theory
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Abstract
In this paper, we present relations between actual trends in economic research and novel methods in data analysis. As the representation of the new data analysis approach we choose the blind signal separation methods. Blind separation methods is a rapidly developed branch of data analysis. It started from some neurophysiological problem and grew to wide range analytical approaches which currently are considered in term of data separation, representation and transformation. The main techniques in this area are: independent component analysis, nonnegative matrix factorization or AMUSE and SOBI algorithms. In the second part of paper we consider knowledge discovery differences in inductive-exploration approach what is typical for blind signal separation methods and apriori-deductive approach what is typical for orthodox economy theories. Against often opposite treatments we suggest complementary interpretation. In our meaning the natural methodological choice is associated with relation data to expert knowledge about given phenomena. In the small data case we need to compensate it by some theorethical assumptions.(original abstract)
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