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

Producción científica: Capítulo de libro › Capítulo › Investigación › revisión exhaustiva

Resumen

© 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	Inglés
Título de la publicación alojada	Trends in Mathematics
Páginas	29-34
Número de páginas	5
Volumen	7
DOI	https://doi.org/10.1007/978-3-319-55639-0_5
Estado	Publicada - 1 ene 2017

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

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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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