Time-domain estimation of time-varying linear systems

1 April 2005

journal article
research article
Published by Taylor & Francis in Journal of Nonparametric Statistics

Vol. 17 (3) , 365-383
https://doi.org/10.1080/10485250500038728

Abstract

In this work, we deal with the problem of estimation of the time-varying coefficients of a linear system, where the input and output are locally stationary processes. In our approach, we propose two types of estimators, kernel and wavelet estimators. They are time-domain estimators in the sense that they involve Yule–Walker type equations and ordinary least squares method. We provide some simulation results and briefly discuss the (asymptotic) statistical properties of the estimators.

Keywords

This publication has 14 references indexed in Scilit:

Nonlinear Wavelet Estimation of Time-Varying Autoregressive Processes
Bernoulli, 1999
Statistical Modeling by Wavelets
Wiley Series in Probability and Statistics, 1999
Efficient estimation of conditional variance functions in stochastic regression
Biometrika, 1998
Local polynomial estimators of the volatility function in nonparametric autoregression
Journal of Econometrics, 1997
Fitting time series models to nonstationary processes
The Annals of Statistics, 1997
Asymptotic statistical inference for nonstationary processes with evolutionary spectra
Published by Springer Nature ,1996
Wavelets on the Interval and Fast Wavelet Transforms
Applied and Computational Harmonic Analysis, 1993
Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data
Published by American Mathematical Society (AMS) ,1993
Ten Lectures on Wavelets
Published by Society for Industrial & Applied Mathematics (SIAM) ,1992
Distributed Lag Approximation to Linear Time-Invariant Systems
The Annals of Statistics, 1979