Abstract
Bounded time series and time series of continuous proportions are often encountered in statistical modeling. Usually, they are addressed either by a logistic transformation of the data, or by specific probability distributions, such as Beta distribution. Nevertheless, these approaches may become quite tricky when the data show an over-dispersion in 0 and/or 1. In these cases, the zero-and/or-one Beta-inflated distributions, \({\mathcal {ZOIB}}\), are preferred. This manuscript combines \({\mathcal {ZOIB}}\) distributions with hidden-Markov models and proposes a flexible model, able to capture several regimes controlling the behavior of a time series of continuous proportions. For illustrating the practical interest of the proposed model, several examples on simulated data are given, as well as a case study on historical data, involving the military logistics of the Duchy of Savoy during the XVIth and the XVIIth centuries.
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Notes
- 1.
The density is taken with respect to the probability measure \(\lambda +\delta _{0}+\delta _{1}\), where \(\lambda \) is the Lebesgue measure on [0, 1], and \(\delta _{0}\) and \(\delta _{1}\) are Dirac masses in 0 and 1.
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Alerini, J., Cottrell, M., Olteanu, M. (2017). Hidden-Markov Models for Time Series of Continuous Proportions with Excess Zeros. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_18
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DOI: https://doi.org/10.1007/978-3-319-59147-6_18
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