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An overview of statistical decomposition techniques applied to complex systems
Date
2008-01-20
Author
Tuncer, Yalcin
Tanik, Murat M.
Allison, David B.
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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.
Subject Keywords
Statistics and Probability
,
Computational Theory and Mathematics
,
Applied Mathematics
,
Computational Mathematics
URI
https://hdl.handle.net/11511/66351
Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
DOI
https://doi.org/10.1016/j.csda.2007.09.012
Collections
Department of Statistics, Article
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Y. Tuncer, M. M. Tanik, and D. B. Allison, “An overview of statistical decomposition techniques applied to complex systems,”
COMPUTATIONAL STATISTICS & DATA ANALYSIS
, pp. 2292–2310, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66351.