ESTIMATION AND BLIND DECONVOLUTION OF AUTOREGRESSIVE SYSTEMS WITH NONSTATIONARY BINARY INPUTS

DOI10.1111/J.1467-9892.1993.TB00167.XzbMath0780.62068OpenAlexW2122534086MaRDI QIDQ4272775

Ta-Hsin Li

Publication date: 20 December 1993

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.1993.tb00167.x

zbMATH Keywords

noisy datasimulation resultsconsistent estimatorblind deconvolutionequalizerAR systemautoregressive systemsmoving-average filterindependent nonstationary binary inputsunobservable binary input

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Non-Markovian processes: estimation (62M09) Identification in stochastic control theory (93E12) Probabilistic methods, stochastic differential equations (65C99)

Related Items (1)

Speed of convergence for the blind deconvolution of a linear system with discrete random input

Cites Work

• Unnamed Item
• Maximum standardized cumulant deconvolution of non-Gaussian linear processes
• Time series: theory and methods
• New criteria for blind deconvolution of nonminimum phase systems (channels)
• Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications
• Blind identification and deconvolution of linear systems driven by binary random sequences
• Channel identification for high speed digital communications
• Martingale Central Limit Theorems

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