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Twenty-One ML Estim...
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Gustafsson, FredrikLinköpings universitet,Reglerteknik,Tekniska högskolan
(author)
Twenty-One ML Estimators for Model Selection
Publisher, publication year, extent ...
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Linköping :Linköping University,1993
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Numbers
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LIBRIS-ID:oai:DiVA.org:liu-55607
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-55607URI
Supplementary language notes
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Language:English
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Summary in:English
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Subject category:vet swepub-contenttype
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Subject category:rap swepub-publicationtype
Series
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LiTH-ISY-R,1400-3902 ;1517
Notes
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Classical approaches to determine a suitable model structure from observed input-output data are based on hypothesis tests and information-based criteria. Recently, the model structure has been considered as a stochastic variable, and standard estimation techniques have been proposed. The resulting estimators are closely related to the aforementioned methods. However, it turns out that there are a number of prior choices in the problem formulation, which are crucial for the estimators' behavior. The contribution of this paper is to clarify the role of the prior choices, to examine a number of possibilities and to show which estimators are consistent. This is done in a linear regression framework. For autoregressive models, we also investigate a novel prior assumption on stability, and give the estimator for the model order and the parameters themselves.
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Hjalmarsson, HåkanLinköpings universitet,Reglerteknik,Tekniska högskolan
(author)
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Linköpings universitetReglerteknik
(creator_code:org_t)
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