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

Research output: Chapter in Book › Chapter › Research › peer-review

Abstract

© 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.

Original language	English
Title of host publication	Trends in Mathematics
Pages	29-34
Number of pages	5
Volume	7
DOIs	https://doi.org/10.1007/978-3-319-55639-0_5
Publication status	Published - 1 Jan 2017

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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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BT - Trends in Mathematics

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

Abstract

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