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S-estimation of hidden Markov models

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  • Alessio Farcomeni
  • Luca Greco

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. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Alessio Farcomeni & Luca Greco, 2015. "S-estimation of hidden Markov models," Computational Statistics, Springer, vol. 30(1), pages 57-80, March.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:1:p:57-80
    DOI: 10.1007/s00180-014-0521-2
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    References listed on IDEAS

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    2. Sugasawa, Shonosuke & Kobayashi, Genya, 2022. "Robust fitting of mixture models using weighted complete estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

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