Under-reported data analysis with INAR-hidden Markov chains

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)4875-4890
JournalStatistics in Medicine
Volume35
Issue number26
DOIs
Publication statusPublished - 20 Nov 2016

Keywords

  • discrete time series
  • emission probabilities
  • integer-autoregressive models
  • thinning operator
  • under-recorded data

Fingerprint

Dive into the research topics of 'Under-reported data analysis with INAR-hidden Markov chains'. Together they form a unique fingerprint.

Cite this