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EnglishWe show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically, we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap-based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.
Tumminello, M., Lillo, F., & Mantegna, R. (2007). Hierarchically nested factor model from multivariate data. EUROPHYSICS LETTERS, 78(3), 30006-p1-30006-p6.
| Data di pubblicazione: | 2007 |
| Titolo: | Hierarchically nested factor model from multivariate data |
| Autori: | |
| Citazione: | Tumminello, M., Lillo, F., & Mantegna, R. (2007). Hierarchically nested factor model from multivariate data. EUROPHYSICS LETTERS, 78(3), 30006-p1-30006-p6. |
| Rivista: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1209/0295-5075/78/30006 |
| Abstract: | We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically, we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap-based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records. |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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http://hdl.handle.net/10447/14110
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