An overview of statistical decomposition techniques applied to complex systems

Authors:
Yalcin Tuncer;Murat M. Tanik;David B. Allison
Affiliations:
Middle East Technical University, Ankara, Turkey and Ankara University, Ankara, Turkey;Department of Electrical and Computer Engineering, U.A.B. Birmingham, AL 35294-4461, USA;Department of Biostatistics, School of Public Health, U.A.B. Birmingham, Alabama, USA
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

The current state of the art in applied decomposition techniques is summarized within a comparative uniform framework. These techniques are classified by the parametric or information theoretic approaches they adopt. An underlying structural model common to all parametric approaches is outlined. The nature and premises of a typical information theoretic approach are stressed. Some possible application patterns for an information theoretic approach are illustrated. Composition is distinguished from decomposition by pointing out that the former is not a simple reversal of the latter. From the standpoint of application to complex systems, a general evaluation is provided.