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Author
Kosiorowski Daniel (Cracow University of Economics, Poland), Mielczarek Dominik (AGH University of Science and Technology Kraków, Poland), Rydlewski Jerzy (AGH University of Science and Technology Kraków, Poland), Snarska Małgorzata (Cracow University of Economics, Poland)
Title
Sparse Methods for Analysis of Sparse Multivariate Data from Big Economic Databases
Source
Statistics in Transition, 2014, vol. 15, nr 1, s. 111-132, rys., tab., bibliogr. 17 poz.
Keyword
Analiza danych, Metodologia badań statystycznych, Metody statystyczne, Bazy danych, Analiza danych funkcjonalnych
Data analysis, Methodology of statistical surveys, Statistical methods, Databases, Functional data analysis
Note
Materiały z konferencji Multivariate Statistical Analysis 2013, Łódź.
Daniel Kosiorowski thanks for financial support from Polish National Science Center grant UMO-2011/03/B/HS4/01138.
summ.
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. (original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of the Wroclaw University of Economics
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Bibliography
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ISSN
1234-7655
Language
eng
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