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 BookChapterResearchpeer-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 languageEnglish
Title of host publicationTrends in Mathematics
Pages29-34
Number of pages5
Volume7
ISBN (Electronic)2297-024X
DOIs
Publication statusPublished - 1 Jan 2017

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