Classification with a Mixture Model Having an Increasing Number of Components

Pons, Odile

doi:10.1007/978-3-642-01044-6_24

Odile Pons⁵

Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization ((STUDIES CLASS))

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Abstract

This paper concerned is with estimation of the components and classification in semi-parametric mixture models with increasing number of components as the sample size grows. Properties of the penalized maximum likelihood estimators are presented: consistency, rates of convergence and asymptotic normality, under additional assumptions. A random classification of the observations is based on the same criterium and some consistency properties are established.

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References

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, Paris, France
Odile Pons

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Odile Pons
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Correspondence to Odile Pons .

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Universität der Bundeswehr, Fak. Wirtschafts-/Sozialwissenschaften, Helmut-Schmidt-Universität, Holstenhofweg 85, Hamburg, 22043, Germany
Andreas Fink
Dept. Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom
Berthold Lausen
Universität der Bundeswehr, Fak. Wirtschafts-/Sozialwissenschaften, Helmut-Schmidt-Universität, Holstenhofweg 85, Hamburg, 22043, Germany
Wilfried Seidel
FB 12 Mathematik und Informatik, Datenbionik AG, Universität Marburg, Hans-Meerwein-Straße, Marburg, 35032, Germany
Alfred Ultsch

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Pons, O. (2009). Classification with a Mixture Model Having an Increasing Number of Components. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_24

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DOI: https://doi.org/10.1007/978-3-642-01044-6_24
Published: 31 July 2009
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01043-9
Online ISBN: 978-3-642-01044-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)

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