A method for robust estimation of dynamic mixtures of multivariate distributions is proposed. The EM algorithm is modified by replacing the classical M-step with high breakdown S-estimation of location and scatter, performed by using the bisquare multivariate S-estimator. Estimates are obtained by solving a system of estimating equations that are characterized by component specific sets of weights, based on robust Mahalanobis-type distances. Convergence of the resulting algorithm is proved and its finite sample behavior is investigated by means of a brief simulation study and n application to a multivariate time series of daily returns for seven stock markets.

Farcomeni, A., L., G. (2015). S-estimation of hidden Markov models. COMPUTATIONAL STATISTICS, 30, 57-80 [10.1007/s00180-014-0521-2].

S-estimation of hidden Markov models

FARCOMENI, Alessio;
2015-01-01

Abstract

A method for robust estimation of dynamic mixtures of multivariate distributions is proposed. The EM algorithm is modified by replacing the classical M-step with high breakdown S-estimation of location and scatter, performed by using the bisquare multivariate S-estimator. Estimates are obtained by solving a system of estimating equations that are characterized by component specific sets of weights, based on robust Mahalanobis-type distances. Convergence of the resulting algorithm is proved and its finite sample behavior is investigated by means of a brief simulation study and n application to a multivariate time series of daily returns for seven stock markets.
2015
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore SECS-S/01 - STATISTICA
English
bisquare; em; hmm
Farcomeni, A., L., G. (2015). S-estimation of hidden Markov models. COMPUTATIONAL STATISTICS, 30, 57-80 [10.1007/s00180-014-0521-2].
Farcomeni, A; L., G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/223787
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