Spectral Density Estimation via Autoregressive Modeling

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F14%3A00075328" target="_blank" >RIV/00216224:14310/14:00075328 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Spectral Density Estimation via Autoregressive Modeling
Original language description
The spectral density function is a commonly used tool when analyzing time series in the frequency domain. Parametric spectral estimation methods have gained attention as potentially interesting tools in the last four decades. They allow the improvement of the statistical properties of spectral estimators with respect to the Fourier-based methods. Estimation of the parameters of ARMA and MA models needs the resolution of a set of nonlinear equations, whereas the AR parameters estimates can be calculatedby solving a set of linear ones. Moreover, algorithms, such as Levinson's, used to solve this set of equations are computationally efficient. When the AR modeling assumption is valid, spectral estimates are less biased and have lower variability than theFourier-based ones. For these reasons, the AR method became the most popular approach to parametric spectral estimation.
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Mathematical Models and Financial Mathematics - Book of short papers
ISBN
9788021066489
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
5-12
Publisher name
Masaryk University
Place of publication
Brno
Event location
Brno
Event date
Jan 1, 2014
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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