Sparse  methods for analysis of sparse multivariate data from big economic databases

Daniel Kosiorowski; Dominik Mielczarek; Jerzy  Rydlewski; Małgorzata Snarska

doi:https://doi.org/10.59170/stattrans-2014-008

Sparse methods for analysis of sparse multivariate data from big economic databases

Daniel Kosiorowski Department of Statistics, Faculty of Management, Cracow University of Economics , Dominik Mielczarek Department of Differential Equations, Faculty of Applied Mathematics, AGH University of Science and Technology , Jerzy Rydlewski Department of Differential Equations, Faculty of Applied Mathematics, AGH University of Science and Technolog , Małgorzata Snarska Department of Capital Markets, Faculty of Finance, Cracow University of Economics Statistics in Transition new series, vol. 15, 2014, 1, pages: 111-132 Published online: 3 March 2014 https://doi.org/10.59170/stattrans-2014-008

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ABSTRACT

In this paper we present a novel perspective dedicated for sparse high-dimensional data sets, i.e. data which contain many zeros among coordinates of observations. Using jointly, selected sparse methods recently proposed in multivariate statistics, and kernel density framework for discrete data, we outline a general perspective for bringing out useful information from big economic databases. As a framework for our considerations we take the so-called functional data analysis, which originates from Ramsay and Silverman works. In particular we use functional principal components analysis within 2D density estimation procedure proposed by Simonoff.

KEYWORDS

sparse data, sparse methods, robust methods, categorical data, big data.

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