Sliced average variance estimation for multivariate time series. Issue 3 (4th May 2019)

• Record Type: Journal Article
• Authors: Matilainen, M.; Croux, C.; Nordhausen, K.; Oja, H.
• Is Part Of: Statistics. Volume 53:Issue 3(2019)
• Page Start: 630
• Page End: 655
• Publication Date: 2019-05-04
• Subjects: Blind source separation -- supervised dimension reduction -- prediction; 62M10; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2019.1605515
• Languages: English
• ISSNs: 0233-1888

Abstract

ABSTRACT: Supervised dimension reduction for time series is challenging as there may be temporal dependence between the response y and the predictors x . 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.