Applying INAR-hidden Markov chains in the analysis of under-reported data

Amanda Fernández-Fontelo; Alejandra Cabaña; Pedro Puig; David Moriña

doi:10.1007/978-3-319-55639-0_5

Applying INAR-hidden Markov chains in the analysis of under-reported data

Amanda Fernández-Fontelo, Alejandra Cabaña, Pedro Puig, David Moriña

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Resum

© 2017, Springer International Publishing AG. We present a model for under-reported time series count data in which the underlying process satisfy an INAR(1) structure. Parameters are estimated through a naïve method based on the theoretical expression of the autocorrelation function of the underlying process, and also by means of the forward algorithm. The hidden process is reconstructed using the Viterbi algorithm, and a real data example is discussed.

Idioma original	Anglès
Títol de la publicació	Trends in Mathematics
Pàgines	29-34
Nombre de pàgines	5
Volum	7
DOIs	https://doi.org/10.1007/978-3-319-55639-0_5
Estat de la publicació	Publicada - 1 de gen. 2017

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10.1007/978-3-319-55639-0_5

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https://www.scopus.com/pages/publications/85019127199

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AB - © 2017, Springer International Publishing AG. We present a model for under-reported time series count data in which the underlying process satisfy an INAR(1) structure. Parameters are estimated through a naïve method based on the theoretical expression of the autocorrelation function of the underlying process, and also by means of the forward algorithm. The hidden process is reconstructed using the Viterbi algorithm, and a real data example is discussed.

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Applying INAR-hidden Markov chains in the analysis of under-reported data

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