Applications of the Periodogram Method for Perturbed Block Toeplitz Matrices in Statistical Signal Processing

Autor: Marta Zárraga-Rodríguez, Xabier Insausti, Jesús Gutiérrez-Gutiérrez
Jazyk: angličtina
Rok vydání: 2020
Předmět:
General Mathematics
02 engineering and technology
01 natural sciences
010305 fluids & plasmas
Periodogram method for perturbed block Toeplitz matrices
vector autoregressive (VAR) processes
Moving average
0103 physical sciences
0202 electrical engineering
electronic engineering
information engineering
Computer Science (miscellaneous)
Parameter estimation
Applied mathematics
the Cholesky decomposition
Engineering (miscellaneous)
Vector moving average (VMA) processes
Mathematics
Block (data storage)
Sequence
Estimation theory
Mathematics::Operator Algebras
lcsh:Mathematics
The Cholesky decomposition
020206 networking & telecommunications
periodogram method for perturbed block Toeplitz matrices
lcsh:QA1-939
Toeplitz matrix
Autoregressive model
Vector autoregressive (VAR) processes
vector moving average (VMA) processes
parameter estimation
Cholesky decomposition
Statistical signal processing
Zdroj: Mathematics, Vol 8, Iss 582, p 582 (2020)
Mathematics
Volume 8
Issue 4
ISSN: 2227-7390
Popis: In this paper, we combine the periodogram method for perturbed block Toeplitz matrices with the Cholesky decomposition to give a parameter estimation method for any perturbed vector autoregressive (VAR) or vector moving average (VMA) process, when we only know a perturbed version of the sequence of correlation matrices of the process. In order to combine the periodogram method for perturbed block Toeplitz matrices with the Cholesky decomposition, we first need to generalize a known result on the Cholesky decomposition of Toeplitz matrices to perturbed block Toeplitz matrices.
Databáze: OpenAIRE
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