Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models

DOI10.1007/s10260-019-00480-yzbMath1436.62247OpenAlexW2952828426WikidataQ127643153 ScholiaQ127643153MaRDI QIDQ1985960

Roberto Rocci, Stefano Antonio Gattone, Roberto Di Mari

Publication date: 7 April 2020

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-019-00480-y

zbMATH Keywords

model selectionequivariant estimatorsclusterwise linear regressioncomputationally efficient approachdata-driven constraintsmixtures of linear regression models

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Cites Work

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• Estimating the components of a mixture of normal distributions
• A New Approach to Estimating Switching Regressions
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