Journal article
Fourier methods for efficient sufficient dimension reduction in time series
The Canadian journal of statistics = La revue canadienne de statistique, Vol.online ahead of print
10/30/2025
Web of Science ID: WOS:001604131600001
Metrics
7 Record Views
Abstract
Dimension reduction has always been one of the most significant and challenging problems in the analysis of high-dimensional data. In the context of time series analysis, our focus is on the estimation and inference of conditional mean and variance functions. By using central mean and variance dimension reduction subspaces that preserve sufficient information about the response, one can estimate the unknown mean and variance functions. While several approaches exist to estimate the time series central mean and variance subspaces (TS-CMS and TS-CVS), they are often computationally intensive and impractical. By employing the Fourier transform, we derive explicit estimators for TS-CMS and TS-CVS. These estimators are consistent, asymptotically normal, and efficient. Simulation studies evaluate the method's performance, showing it is significantly more accurate and computationally efficient than existing ones. Furthermore, the method is applied to the Canadian lynx dataset.</span></p>
Related links
Details
- Title
- Fourier methods for efficient sufficient dimension reduction in time series
- Publication Details
- The Canadian journal of statistics = La revue canadienne de statistique, Vol.online ahead of print
- Resource Type
- Journal article
- Publisher
- John Wiley & Sons, Inc.
- Number of pages
- 25
- Copyright
- © 2025 Statistical Society of Canada | Société statistique du Canada.
- Identifiers
- WOS:001604131600001; 99381517021706600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English