Deterministic regression smoothness priors TVAR modelling

Author

Kaipio, J.P. ; Juntunen, M.

Author_Institution

Dept. of Appl. Phys., Kuopio Univ., Finland

Volume

3

fYear

1999

fDate

15-19 Mar 1999

Firstpage

1693

Abstract

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

Keywords

autoregressive processes; inverse problems; parameter estimation; prediction theory; smoothing methods; time-varying systems; transient analysis; TVAR modelling; coefficient evolutions; deterministic regression smoothness priors; heavily underdetermined unconstrained prediction equations; nonhomogeneous smoothness properties; smoothness priors type side constraint; subspace; time-varying autoregressive processes; transient oscillations; Adaptive algorithm; Autoregressive processes; Brain modeling; Equations; Least squares approximation; Parametric statistics; Physics; Resonance light scattering; Stochastic processes; Subspace constraints;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on

Conference_Location

Phoenix, AZ

ISSN

1520-6149

Print_ISBN

0-7803-5041-3

Type

conf

DOI

10.1109/ICASSP.1999.756319

Filename

756319