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The Gaussian MLE versus the Optimally weighted LSE
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). (System Identification)ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). (System Identification)ORCID iD: 0000-0002-9368-3079
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). (System Identification)ORCID iD: 0000-0002-1927-1690
2020 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 37, no 6, p. 195-199Article in journal (Refereed) Published
Abstract [en]

In this note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian Maximum Likelihood Estimator (MLE), and the optimally weighted Least-Squares Estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized, and consider Gaussian and non-Gaussian data distributions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2020. Vol. 37, no 6, p. 195-199
Keywords [en]
Gaussian MLE; optimally weighted LSE; least squares; optimal weighting; semi-parametric models; parameter estimation; system identification
National Category
Signal Processing Control Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-273764DOI: 10.1109/MSP.2020.3019236ISI: 000587684700018Scopus ID: 2-s2.0-85096226071OAI: oai:DiVA.org:kth-273764DiVA, id: diva2:1432951
Funder
Swedish Research Council, 2016-06079 (NewLEADS), 2015-05285, and 2019-04956
Note

QC 20200529

Available from: 2020-05-28 Created: 2020-05-28 Last updated: 2022-06-26Bibliographically approved

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Abdalmoaty, MohamedHjalmarsson, HåkanWahlberg, Bo

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