A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Sliced average variance estimation for multivariate time series
Tekijät: M. Matilainen, C. Croux, K. Nordhausen, H. Oja
Kustantaja: TAYLOR & FRANCIS LTD
Julkaisuvuosi: 2019
Journal: Statistics
Tietokannassa oleva lehden nimi: STATISTICS
Lehden akronyymi: STATISTICS-ABINGDON
Vuosikerta: 53
Numero: 3
Aloitussivu: 630
Lopetussivu: 655
Sivujen määrä: 26
ISSN: 0233-1888
DOI: https://doi.org/10.1080/02331888.2019.1605515
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/40479543
Tiivistelmä
Supervised dimension reduction for time series is challenging as there may be temporal dependence between the response y and the predictors . Recently a time series version of sliced inverse regression, TSIR, was suggested, which applies approximate joint diagonalization of several supervised lagged covariance matrices to consider the temporal nature of the data. In this paper, we develop this concept further and propose a time series version of sliced average variance estimation, TSAVE. As both TSIR and TSAVE have their own advantages and disadvantages, we consider furthermore a hybrid version of TSIR and TSAVE. Based on examples and simulations we demonstrate and evaluate the differences between the three methods and show also that they are superior to apply their iid counterparts to when also using lagged values of the explaining variables as predictors.
Supervised dimension reduction for time series is challenging as there may be temporal dependence between the response y and the predictors . Recently a time series version of sliced inverse regression, TSIR, was suggested, which applies approximate joint diagonalization of several supervised lagged covariance matrices to consider the temporal nature of the data. In this paper, we develop this concept further and propose a time series version of sliced average variance estimation, TSAVE. As both TSIR and TSAVE have their own advantages and disadvantages, we consider furthermore a hybrid version of TSIR and TSAVE. Based on examples and simulations we demonstrate and evaluate the differences between the three methods and show also that they are superior to apply their iid counterparts to when also using lagged values of the explaining variables as predictors.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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