The empirical likelihood method applied to covariance matrix estimation

DOI10.1016/j.sigpro.2009.07.028zbMath1177.94068OpenAlexW1996309066MaRDI QIDQ1048855

Pascal Larzabal, Hugo Harari-Kermadec, Frédéric P. Pascal

Publication date: 8 January 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2009.07.028

zbMATH Keywords

maximum likelihoodempirical likelihoodnon-Gaussian noisecovariance matrix estimationstructured parameters estimation

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Statistical aspects of information-theoretic topics (62B10)

Cites Work

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• Empirical likelihood ratio confidence regions
• Empirical likelihood and general estimating equations
• Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter
• Channel identification for high speed digital communications
• Asymptotic Statistics
• Covariance Structure Maximum-Likelihood Estimates in Compound Gaussian Noise: Existence and Algorithm Analysis
• Performance Analysis of Covariance Matrix Estimates in Impulsive Noise
• Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
• Signal detection in Gaussian noise of unknown level: An invariance application
• The 1972 Wald Lecture Robust Statistics: A Review

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