Empirical Bayes identification of stationary processes and approximation of Toeplitz spectra

DOI10.1016/j.automatica.2022.110362zbMath1494.93110OpenAlexW4229374745WikidataQ114204795 ScholiaQ114204795MaRDI QIDQ2151875

Yanyan Li

Publication date: 5 July 2022

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2022.110362

zbMATH Keywords

subspace methodsfrequency estimationprolate spheroidal wave functionsempirical Bayes estimationToeplitz spectra approximation

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Frequency-response methods in control theory (93C80) Identification in stochastic control theory (93E12)

Cites Work

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• Two modeling strategies for empirical Bayes estimation
• Consistency of subspace methods for signals with almost-periodic components
• On covariance function tests used in system identification
• Bandpass sampling and bandpass analogues of prolate spheroidal functions
• Eigenvalue distribution of time and frequency limiting
• Superiority of empirical Bayes estimator of the mean vector in multivariate normal distribution
• Analysis of Panel Data
• Spectral Analysis of Signals: The Missing Data Case
• Mean Squared Error Properties of Empirical Bayes Estimators in a Multivariate Random Effects General Linear Model
• Prolate Spheroidal Wave Functions, Fourier Analysis, and Uncertainty-V: The Discrete Case
• Linear Stochastic Systems
• Prolate Spheroidal Wave Functions, Fourier Analysis and Uncertainty - I
• Prolate Spheroidal Wave Functions, Fourier Analysis and Uncertainty - II
• RELATIONS BETWEEN TWO SETS OF VARIATES
• The Spectra of Toeplitz's Matrices