Deterministic regression smoothness priors TVAR modelling
- Resource Type
- Conference
- Authors
- Kaipio, J.P.; Juntunen, M.
- Source
- 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) Acoustics, speech, and signal processing Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on. 3:1693-1696 vol.3 1999
- Subject
- Signal Processing and Analysis
Components, Circuits, Devices and Systems
Brain modeling
Subspace constraints
Adaptive algorithm
Least squares approximation
Resonance light scattering
Stochastic processes
Physics
Autoregressive processes
Equations
Parametric statistics
- Language
- ISSN
- 1520-6149
In this paper we propose a method for the estimation of time-varying autoregressive (TVAR) processes. The approach is essentially to regularize the heavily underdetermined unconstrained prediction equations with a smoothness priors type side constraint. The implementation of nonhomogeneous smoothness properties is straightforward. The method is compared to the usual deterministic regression approach (TVAR) in which the coefficient evolutions are constrained to a subspace. It is shown that the typical transient oscillations of TVAR can be avoided with the proposed method.