Abstract
Latent class modeling has proven to be a powerful tool for identifying regimes in time series. Here, we focus on the classification uncertainty in latent class modeling of time series data with emphasis on entropy-based measures of uncertainty. Results are illustrated with an example.
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References
Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics, 41, 164–171.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B, 39, 1–38.
Dias, J. G., & Vermunt, J. K. (2006). Bootstrap methods for measuring classification uncertainty in latent class analysis. In A. Rizzi & M. Vichi (Eds.), COMPSTAT 2006: proceedings in computational statistics, Rome (pp. 31–41). Heidelberg: Physica-Verlag.
Dias, J. G., & Vermunt, J. K. (2008). A bootstrap-based aggregate classifier for model-based clustering. Computational Statistics, 23(4), 643–659.
Dias, J. G., Vermunt, J. K., & Ramos, S. B. (2008). Heterogeneous hidden Markov models. In P. Brito (Ed.), COMPSTAT 2008: proceedings in computational statistics, Porto (pp. 373–380). Heidelberg: Physica-Verlag.
McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.
Ramos, S. B., Vermunt, J. K., & Dias, J. G. (2011). When markets fall down: Are emerging markets all the same? International Journal of Finance and Economics, 16(4), 324-338.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
Acknowledgements
The author would like to thank the Fundação para a Ciência e a Tecnologia (Portugal) for its financial support (PTDC/EGE-GES/103223/2008 and PEst-OE/EGE/UI0315/ 2011) and the three referees for their very valuable comments.
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Dias, J.G. (2013). Latent Class Models of Time Series Data: An Entropic-Based Uncertainty Measure. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_20
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DOI: https://doi.org/10.1007/978-3-319-00035-0_20
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