Maximum smoothed likelihood for multivariate mixtures
AbstractWe introduce an algorithm for estimating the parameters in a finite mixture of completely unspecified multivariate components in at least three dimensions under the assumption of conditionally independent coordinate dimensions. We prove that this algorithm, based on a majorization-minimization idea, possesses a desirable descent property just as any em algorithm does. We discuss the similarities between our algorithm and a related one, the so-called nonlinearly smoothed em algorithm for the non-mixture setting. We also demonstrate via simulation studies that the new algorithm gives very similar results to another algorithm that has been shown empirically to be effective but that does not satisfy any descent property. We provide code for implementing the new algorithm in a publicly available R package. Copyright 2011, Oxford University Press.
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Bibliographic InfoArticle provided by Biometrika Trust in its journal Biometrika.
Volume (Year): 98 (2011)
Issue (Month): 2 ()
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- Hiroyuki Kasahara & Katsumi Shimotsu, 2012.
"Nonparametric Identification and Estimation of the Number of Components in Multivariate Mixtures,"
CIRJE-F-866, CIRJE, Faculty of Economics, University of Tokyo.
- Kasahara Hiroyuki & Shimotsu Katsumi, 2012. "Nonparametric Identification and Estimation of the Number of Components in Multivariate Mixtures," Global COE Hi-Stat Discussion Paper Series gd12-247, Institute of Economic Research, Hitotsubashi University.
- Stephane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2014. "Nonparametric spectral-based estimation of latent structures," CeMMAP working papers CWP18/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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