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Peña, D.; Yohai, V.J. "Generalized Dynamic Principal Components" (2016) Journal of the American Statistical Association. 111(515):1121-1131
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Abstract:

Brillinger defined dynamic principal components (DPC) for time series based on a reconstruction criterion. He gave a very elegant theoretical solution and proposed an estimator which is consistent under stationarity. Here, we propose a new enterally empirical approach to DPC. The main differences with the existing methods—mainly Brillinger procedure—are (1) the DPC we propose need not be a linear combination of the observations and (2) it can be based on a variety of loss functions including robust ones. Unlike Brillinger, we do not establish any consistency results; however, contrary to Brillinger’s, which has a very strong stationarity flavor, our concept aims at a better adaptation to possible nonstationary features of the series. We also present a robust version of our procedure that allows to estimate the DPC when the series have outlier contamination. We give iterative algorithms to compute the proposed procedures that can be used with a large number of variables. Our nonrobust and robust procedures are illustrated with real datasets. Supplementary materials for this article are available online. © 2016 American Statistical Association.

Registro:

Documento: Artículo
Título:Generalized Dynamic Principal Components
Autor:Peña, D.; Yohai, V.J.
Filiación:Statistics Department, Universidad Carlos II de Madrid, Getafe, Spain
Mathematics Department, Ciudad Universitaria, Buenos Aires, Argentina
Palabras clave:Dimensionality reduction; Reconstruction of data; Vector time series
Año:2016
Volumen:111
Número:515
Página de inicio:1121
Página de fin:1131
DOI: http://dx.doi.org/10.1080/01621459.2015.1072542
Título revista:Journal of the American Statistical Association
Título revista abreviado:J. Am. Stat. Assoc.
ISSN:01621459
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01621459_v111_n515_p1121_Pena

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Citas:

---------- APA ----------
Peña, D. & Yohai, V.J. (2016) . Generalized Dynamic Principal Components. Journal of the American Statistical Association, 111(515), 1121-1131.
http://dx.doi.org/10.1080/01621459.2015.1072542
---------- CHICAGO ----------
Peña, D., Yohai, V.J. "Generalized Dynamic Principal Components" . Journal of the American Statistical Association 111, no. 515 (2016) : 1121-1131.
http://dx.doi.org/10.1080/01621459.2015.1072542
---------- MLA ----------
Peña, D., Yohai, V.J. "Generalized Dynamic Principal Components" . Journal of the American Statistical Association, vol. 111, no. 515, 2016, pp. 1121-1131.
http://dx.doi.org/10.1080/01621459.2015.1072542
---------- VANCOUVER ----------
Peña, D., Yohai, V.J. Generalized Dynamic Principal Components. J. Am. Stat. Assoc. 2016;111(515):1121-1131.
http://dx.doi.org/10.1080/01621459.2015.1072542