2015
DOI: 10.1109/tsp.2015.2469640
|View full text |Cite
|
Sign up to set email alerts
|

Mathematical Framework for Pseudo-Spectra of Linear Stochastic Difference Equations

Abstract: Although spectral analysis of stationary stochastic processes has solid mathematical foundations, this is not always so for some non-stationary cases. Here, we establish a rigorous mathematical extension of the classic Fourier spectrum to the case in which there are AR roots in the unit circle, ie, the transfer function of the linear time-invariant filter has poles on the unit circle. To achieve it we: embed the classical problem in a wider framework, extend the Discrete Time Fourier Transform and defined a ne… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
4

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 38 publications
0
3
0
Order By: Relevance
“…One seeks a WK filter Ψ(B) such that the mean squared error (MSE) of estimating s t via Ψ(B)x t is minimized. The solution is given in [2], and extended to the multivariate case in [27]: essentially, Ψ(e −iλ ) is given by a ratio of pseudospectra [4] for signal to data process. Hence, if a model with latent components has been fitted the spectral densities for signal and noise can be calculated, and the coefficients ψ j computed.…”
Section: Filteringmentioning
confidence: 99%
“…One seeks a WK filter Ψ(B) such that the mean squared error (MSE) of estimating s t via Ψ(B)x t is minimized. The solution is given in [2], and extended to the multivariate case in [27]: essentially, Ψ(e −iλ ) is given by a ratio of pseudospectra [4] for signal to data process. Hence, if a model with latent components has been fitted the spectral densities for signal and noise can be calculated, and the coefficients ψ j computed.…”
Section: Filteringmentioning
confidence: 99%
“…The LDHR is a linear estimation procedure that also provides an automatic identification of the complete DHR model. It exploits the algebraic structure of the pseudo-spectrum functions (see Bujosa, Bujosa, & García-Ferrer, 2015) to avoid the poles associated with the unit modulus AR roots of the pseudo-spectrum of DHR models f dhr (𝜔).…”
Section: Estimating Smooth Trends By Dynamic Harmonic Regression (Dhr)mentioning
confidence: 99%
“…Difference equations (Brand, 1966;Miller, 1968;Bainov and Stamova, 1997) allow a nonlinear system to transform into a linear one. Therefore, this technique is used in many applications of electrical engineering from signal processing to power system tools (Shoihet and Slonim, 2010;Babiarz, 2015;Li et al, 2017;Ngoc, 2018;Shen et al, 2016;Bujosa and Ferrer, 2015;Mirkovic et al, 2014). The nonlinear converter topologies (Flyback, Forward, Push-Pull, halfbridge and full-bridge topologies) are solved by using genetic algorithms in Versele et al (2014).…”
Section: Introductionmentioning
confidence: 99%