Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm
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DOI: 10.1016/j.csda.2011.12.017
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References listed on IDEAS
- Altman, Rachel MacKay, 2007. "Mixed Hidden Markov Models: An Extension of the Hidden Markov Model to the Longitudinal Data Setting," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 201-210, March.
- Frank Rijmen & Edward H. Ip & Stephen Rapp & Edward G. Shaw, 2008. "Qualitative longitudinal analysis of symptoms in patients with primary and metastatic brain tumours," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 739-753, June.
- Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
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Cited by:
- Antonello Maruotti, 2015. "Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 84-109, March.
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Keywords
Nonlinear mixed effects model; SAEM algorithm; Forward backward algorithm; Epileptic seizures count;All these keywords.
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