Abstract.
It is well known that the log-likelihood function for samples coming from normal mixture distributions may present spurious maxima and singularities. For this reason here we reformulate some Hathaway’s results and we propose two constrained estimation procedures for multivariate normal mixture modelling according to the likelihood approach. Their perfomances are illustrated on the grounds of some numerical simulations based on the EM algorithm. A comparison between multivariate normal mixtures and the hot-deck approach in missing data imputation is also considered.
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Salvatore Ingrassia: S. Ingrassia carried out the research as part of the project Metodi Statistici e Reti Neuronali per l’Analisi di Dati Complessi (PRIN 2000, resp. G. Lunetta).
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Ingrassia, S. A likelihood-based constrained algorithm for multivariate normal mixture models. Statistical Methods & Applications 13, 151–166 (2004). https://doi.org/10.1007/s10260-004-0092-4
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DOI: https://doi.org/10.1007/s10260-004-0092-4