A1 Refereed original research article in a scientific journal
Sliced average variance estimation for multivariate time series
Authors: M. Matilainen, C. Croux, K. Nordhausen, H. Oja
Publisher: TAYLOR & FRANCIS LTD
Publication year: 2019
Journal: Statistics
Journal name in source: STATISTICS
Journal acronym: STATISTICS-ABINGDON
Volume: 53
Issue: 3
First page : 630
Last page: 655
Number of pages: 26
ISSN: 0233-1888
DOI: https://doi.org/10.1080/02331888.2019.1605515
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/40479543
Abstract
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.
Downloadable publication This is an electronic reprint of the original article. |  |
UTU Research Portal