Arbeitspapier

Independent nonlinear component analysis

The idea of summarizing the information contained in a large number of variables by a small number of "factors" or "principal components" has been broadly adopted in economics and statistics. This paper introduces a generalization of the widely used principal component analysis (PCA) to nonlinear settings, thus providing a new tool for dimension reduction and exploratory data analysis or representation. The distinguishing features of the method include (i) the ability to always deliver truly independent factors (as opposed to the merely uncorre-lated factors of PCA); (ii) the reliance on the theory of optimal transport and Brenier maps to obtain a robust and efficient computational algorithm; (iii) the use of a new multivariate additive entropy decomposition to determine the principal nonlinear components that capture most of the information content of the data and (iv) formally nesting PCA as a special case, for linear Gaussian factor models. We illustrate the method's effectiveness in an application to the prediction of excess bond returns from a large number of macro factors.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP46/19

Klassifikation
Wirtschaft

Ereignis
Geistige Schöpfung
(wer)
Gunsilius, Florian
Schennach, Susanne M.
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2019

DOI
doi:10.1920/wp.cem.2019.4619
Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Gunsilius, Florian
  • Schennach, Susanne M.
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2019

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