Semi-Markov Modelling for Multi-state Systems

Barbu, Vlad Stefan; Karagrigoriou, Alex; Makrides, Andreas

doi:10.1109/ares.2014.60

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Therefore, a beginning of an epidemic trend is a change point whose timely detection will predict occurrence of a new epidemic. Similar explanations as the ones given above could be used in several scientific fields that incidence data occur such as seismology 31–33 and meteorology. 34…”

Section: Changepoint Detection Periodic Arma Modeling and Estimatmentioning

confidence: 88%

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

Kalligeris

Karagrigoriou

Parpoula

2019

Stat Methods Med Res

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When it comes to incidence data, most of the work on this field focuses on the modeling of nonextreme periods. Several attempts have been made and a variety of techniques are available to achieve so. In this work, in order to model not only the nonextreme periods but also capture the behavior of the whole time-series, we make use of a dataset on influenza-like illness rate for Greece, for the period 2014–2016. The identification of extreme periods is made possible via changepoint detection analysis and model selection techniques are developed in order to identify the optimal periodic-type auto-regressive moving average model with covariates that best describes the pattern of the time-series. In addition, in the context of incidence data modeling, an advanced algorithm was developed in order to improve the accuracy of the selected model. The derived results are satisfactory since the changepoint method seems to identify correctly the extreme periods, and the selected model: (1) estimates accurately the influenza-like illness syndrome morbidity burden in the case of Greece, and (2) captures satisfactorily enough the behavior of the whole time-series.

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Section: Changepoint Detection Periodic Arma Modeling and Estimatmentioning

confidence: 88%

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

Kalligeris

Karagrigoriou

Parpoula

2019

Stat Methods Med Res

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“…), there is an interest in choosing the minimum of them. (b) Note that this framework has been considered also in [4], but for different family of distributions. (c) This could be an interesting and rich framework for modelling time varying parameters with a similar approach like in [6].…”

Section: Remark 3 (A)mentioning

confidence: 99%

Statistical Inference for Multi State Systems under the Generalized Modified Weibull Class

Makrides

2022

JRSS

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Multi state systems can be seen as semi-Markov processes by considering an arbitrary distribution function for sojourn times. Especially, in this work, the Modified Weibull distribution is employed to be the distribution of sojourn times with a shape parameter λλ such that is member of a distributions family that is closed under minima. Parameters estimators are provided and the proposed methodology is evaluated using a detailed simulation procedure.

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