Robust \(m\)-interval detection procedures for strong mixing noise

DOI10.1016/0020-0255(95)00016-IzbMath0868.94008OpenAlexW2051308197MaRDI QIDQ1357085

Ludwik Kurz, Arshad Hussain

Publication date: 16 June 1997

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0020-0255(95)00016-i

zbMATH Keywords

non-Gaussianstrong mixing noiserobust partition detectors

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Detection theory in information and communication theory (94A13)

Cites Work

• On a Theorem of Pitman
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• Some Limit Theorems for Random Functions. I
• An analysis of a nonparametric detection scheme for strong mixing noise (Corresp.)
• Recursive probability density estimation for weakly dependent stationary processes
• Discrete-time detection in<tex>epsilon</tex>-mixing noise
• On the performance of a nonparametric detection scheme for dependent data (Corresp.)
• Improved operation of m-interval detectors by optimum signal selection
• Nonparametric detection using dependent samples (Corresp.)
• The Invariance Principle for Stationary Processes
• Nonparametric detectors based on m-interval partitioning
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