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The Safety Belt estimator under multivariate linear models with inequality constraints

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Τίτλος: The Safety Belt estimator under multivariate linear models with inequality constraints
Συγγραφείς: Filipiak, Katarzyna, von Rosen, Dietrich, Rejchel, Wojciech, Singull, Martin
Πηγή: Journal of Statistical Planning and Inference. 241
Θεματικοί όροι: Convex optimization theory, Inequality constraints, MANOVA model, Maximum likelihood estimation
Περιγραφή: The main goal of this paper is to determine maximum likelihood estimators under a multivariate linear model with prior information introduced via inequality restrictions on the mean parameters. The restrictions are in the form of quadratic inequalities. Methods from convex optimization theory play a fundamental role in determining the estimators. A characteristic of the new estimators, called Safety Belt estimators, is that depending on the observed data, there are two alternative solutions to the likelihood equations.
Περιγραφή αρχείου: electronic
Σύνδεσμος πρόσβασης: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-217538
https://doi.org/10.1016/j.jspi.2025.106335
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Items – Name: Title
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  Data: The Safety Belt estimator under multivariate linear models with inequality constraints
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  Data: <searchLink fieldCode="AR" term="%22Filipiak%2C+Katarzyna%22">Filipiak, Katarzyna</searchLink><br /><searchLink fieldCode="AR" term="%22von+Rosen%2C+Dietrich%22">von Rosen, Dietrich</searchLink><br /><searchLink fieldCode="AR" term="%22Rejchel%2C+Wojciech%22">Rejchel, Wojciech</searchLink><br /><searchLink fieldCode="AR" term="%22Singull%2C+Martin%22">Singull, Martin</searchLink>
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  Data: <i>Journal of Statistical Planning and Inference</i>. 241
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Convex+optimization+theory%22">Convex optimization theory</searchLink><br /><searchLink fieldCode="DE" term="%22Inequality+constraints%22">Inequality constraints</searchLink><br /><searchLink fieldCode="DE" term="%22MANOVA+model%22">MANOVA model</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+estimation%22">Maximum likelihood estimation</searchLink>
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  Data: The main goal of this paper is to determine maximum likelihood estimators under a multivariate linear model with prior information introduced via inequality restrictions on the mean parameters. The restrictions are in the form of quadratic inequalities. Methods from convex optimization theory play a fundamental role in determining the estimators. A characteristic of the new estimators, called Safety Belt estimators, is that depending on the observed data, there are two alternative solutions to the likelihood equations.
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      – SubjectFull: Convex optimization theory
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      – SubjectFull: Inequality constraints
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      – SubjectFull: MANOVA model
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              Y: 2026
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