Abstract
Self-similar processes have a rich linear structure, based on scale invariance, which is analogous to the shift-invariant structure of stationary processes. The analogy is made explicit via Lamperti's perti's transformation. This transformation is used here to characterize the reproducing kernel Hilbert space (RKHS) associated with self-similar processes and hence to solve problems of prediction, whitening, and Gaussian signal detection. Some specific results for the fractional Brownian motion illustrate the general concepts.
Original language | English (US) |
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Pages (from-to) | 498 |
Number of pages | 1 |
Journal | IEEE International Symposium on Information Theory - Proceedings |
DOIs | |
State | Published - 2000 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics