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Your search keyword '"Tumminello, Michele"' showing total 4 results
Start Over Author "Tumminello, Michele" Topic correlation analysis
4 results on '"Tumminello, Michele"'

1. Spectral properties of correlation matrices for some hierarchically nested factor models.

Author

Tumminello, Michele, Lillo, Fabrizio, and Mantegna, Rosario N.

Subjects
*MATRICES (Mathematics), *UNIVERSAL algebra, *RANDOM matrices, *PRINCIPAL components analysis, *ALGORITHMS
Abstract

We show that spectral methods, such as Principal Component Analysis and Random Matrix Theory, are unable to reveal the hierarchical (or nested) structure of a set of mutivariate data. We consider the method introduced in M. Tumminello et al., EPL 78, 30006 (2007) to associate a hierarchical factor model with a set of data by making use of clustering algorithms. This is done by proving the existence of a bijective correspondence between a hierarchical tree and a factor model. [ABSTRACT FROM AUTHOR]

Published
2007
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2. SPANNING TREES AND BOOTSTRAP RELIABILITY ESTIMATION IN CORRELATION-BASED NETWORKS.

Author

TUMMINELLO, MICHELE, CORONNELLO, CLAUDIA, LILLO, FABRIZIO, MICCICHÈ, SALVATORE, and MANTEGNA, ROSARIO N.

Subjects
*STATISTICAL bootstrapping, *DISTRIBUTION (Probability theory), *STATISTICAL sampling, *CLUSTER analysis (Statistics)
Abstract

We introduce a new technique to associate a spanning tree to the average linkage cluster analysis. We term this tree as the Average Linkage Minimum Spanning Tree. We also introduce a technique to associate a value of reliability to the links of correlation-based graphs by using bootstrap replicas of data. Both techniques are applied to the portfolio of the 300 most capitalized stocks traded on the New York Stock Exchange during the time period 2001–2003. We show that the Average Linkage Minimum Spanning Tree recognizes economic sectors and sub-sectors as communities in the network slightly better than the Minimum Spanning Tree. We also show that the average reliability of links in the Minimum Spanning Tree is slightly greater than the average reliability of links in the Average Linkage Minimum Spanning Tree. [ABSTRACT FROM AUTHOR]

Published
2007
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3. Structure and evolution of a European Parliament via a network and correlation analysis.

Author

Puccio, Elena, Pajala, Antti, Piilo, Jyrki, and Tumminello, Michele

Subjects
*STATISTICAL correlation, *COMMUNITY organization, *SOCIAL systems, *POLITICAL opposition, *POLITICAL science
Abstract

We present a study of the network of relationships among elected members of the Finnish parliament, based on a quantitative analysis of initiative co-signatures, and its evolution over 16 years. To understand the structure of the parliament, we constructed a statistically validated network of members, based on the similarity between the patterns of initiatives they signed. We looked for communities within the network and characterized them in terms of members’ attributes, such as electoral district and party. To gain insight on the nested structure of communities, we constructed a hierarchical tree of members from the correlation matrix. Afterwards, we studied parliament dynamics yearly, with a focus on correlations within and between parties, by also distinguishing between government and opposition. Finally, we investigated the role played by specific individuals, at a local level. In particular, whether they act as proponents who gather consensus, or as signers. Our results provide a quantitative background to current theories in political science. From a methodological point of view, our network approach has proven able to highlight both local and global features of a complex social system. [ABSTRACT FROM AUTHOR]

Published
2016
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4. COVARIANCE AND CORRELATION ESTIMATORS IN BIPARTITE SYSTEMS

Author

Puccio, E., Puccio, E., TUMMINELLO, Michele, and PALMA, Gioacchino Massimo

Subjects
Bipartite systems, Biological systems, Wallenius non-central Hypergeometric Distribution, Correlation Analysis, Complex Systems, Social systems, Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin)
Abstract

We present a weighted estimator of the covariance and correlation in bipartite complex systems with a double layer of heterogeneity. The advantage provided by the weighted estimators lies in the fact that the unweighted sample covariance and correlation can be shown to possess a bias. Indeed, such a bias affects real bipartite systems, and, for example, we report its effects on two empirical systems, one social and the other biological. On the contrary, our newly proposed weighted estimators remove the bias and are better suited to describe such systems.

Published
2017

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