Under-reported data analysis with INAR-hidden Markov chains

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

doi:10.1002/sim.7026

Under-reported data analysis with INAR-hidden Markov chains

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

Producción científica: Contribución a una revista › Artículo › Investigación › revisión exhaustiva

35 Citas (Scopus)

Resumen

Copyright © 2016 John Wiley & Sons, Ltd. In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John Wiley & Sons, Ltd.

Idioma original	Inglés
Páginas (desde-hasta)	4875-4890
Publicación	Statistics in Medicine
Volumen	35
N.º	26
DOI	https://doi.org/10.1002/sim.7026
Estado	Publicada - 20 nov 2016

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title = "Under-reported data analysis with INAR-hidden Markov chains",

abstract = "Copyright {\textcopyright} 2016 John Wiley & Sons, Ltd. In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A na{\"i}ve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright {\textcopyright} 2016 John Wiley & Sons, Ltd.",

keywords = "discrete time series, emission probabilities, integer-autoregressive models, thinning operator, under-recorded data",

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AU - Fernández-Fontelo, Amanda

AU - Cabaña, Alejandra

AU - Puig, Pedro

AU - Moriña, David

PY - 2016/11/20

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AB - Copyright © 2016 John Wiley & Sons, Ltd. In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John Wiley & Sons, Ltd.

KW - discrete time series

KW - emission probabilities

KW - integer-autoregressive models

KW - thinning operator

KW - under-recorded data

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DO - 10.1002/sim.7026

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SN - 0277-6715

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SP - 4875

EP - 4890

JO - Statistics in Medicine

JF - Statistics in Medicine

IS - 26

ER -

Under-reported data analysis with INAR-hidden Markov chains

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