Result: A Single-Pass Algorithm for Spectrum Estimation With Fast Convergence
Title:
A Single-Pass Algorithm for Spectrum Estimation With Fast Convergence
Authors:
Source:
IEEE transactions on information theory. 57(7):4720-4731
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2011.
Publication Year:
2011
Physical Description:
print, 40 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie de l'information, Information theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Représentation du signal. Analyse spectrale, Signal representation. Spectral analysis, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Algorithme récursif, Recursive algorithm, Algoritmo recursivo, Analyse signal, Signal analysis, Análisis de señal, Analyse spectre, Spectrum analysis, Análisis espectro, Covariance, Covariancia, Densité spectrale, Spectral density, Densidad espectral, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Estimation signal, Signal estimation, Estimación señal, Fonction logarithmique, Logarithmic function, Función logarítmica, Mise à jour, Updating, Actualización, Méthode récursive, Recursive method, Método recursivo, Processus stationnaire, Stationary process, Proceso estacionario, Processus stochastique, Stochastic process, Proceso estocástico, Procédé discontinu, Batch process, Procedimiento discontínuo, Simulation, Simulación, Taux convergence, Convergence rate, Relación convergencia, Batched mean estimate, bias reduction, nonparametric estimation, physical dependence measure, recursive algorithm, spectral density, stochastic process
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, The University of Chicago, Chicago, IL 60637, United States
ISSN:
0018-9448
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.24327950
Database:
PASCAL Archive
Further Information
We propose a single-pass algorithm for estimating spectral densities of stationary processes. Our algorithm is computationally fast in the sense that, when a new observation arrives, it can provide a real-time update within O(1) computation. The proposed algorithm is probabilistically fast in that, for stationary processes whose auto-covariances decay geometrically, the estimates from the algorithm converge at a rate which is optimal up to a multiplicative logarithmic factor. We also establish asymptotic normality for the recursive estimate. A simulation study is carried out and it confirms the superiority over the classical batched mean estimates.