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Optimal Dimension Reduction for High-dimensional and Functional Time Series

Marc Hallin (), Siegfried Hörmann and Marco Lippi ()

No ECARES 2017-39, Working Papers ECARES from ULB -- Universite Libre de Bruxelles

Abstract: Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and functional observations. Whether the data are vector- or function-valued, principal component techniques, in this context, play a central role. The success of principal components in the dimension reduction problem is explained by the fact that, for any K

Keywords: dimension reduction; time series; principal components; functional principal components; dynamic principal components; Karhunen-Loève expansion (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2017-11
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Journal Article: Optimal dimension reduction for high-dimensional and functional time series (2018) Downloads
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