| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Fed Bahia, Inst Math & Stat, Salvador, BA - Brazil
[2] Univ Sao Paulo, Inst Math & Stat, Sao Paulo - Brazil
[3] Fed Univ ABC, Ctr Math Computat & Cognit, Santo Andre - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | COMPUTATIONAL STATISTICS & DATA ANALYSIS; v. 130, p. 61-79, FEB 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
A causal wavelet decomposition of the covariance structure for bivariate locally stationary processes, named directed wavelet covariance, is introduced and discussed. Theoretically, when compared to Fourier-based quantities, wavelet-based estimators are more appropriate to non-stationary processes and processes with local patterns, outliers and rapid regime changes. Results of directed coherence (DC), wavelet coherence (WTC) and directed wavelet covariance (DWC) with simulated data are also presented. All three quantities could identify the simulated covariances structures. Finally, an illustration of the proposed directed wavelet covariance in a task-based EEG experiment is given. (C) 2018 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 13/00506-1 - Time series, wavelets and functional data analysis |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |