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• W2475156936 abstract "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." @default.
• W2475156936 created "2016-07-22" @default.
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• W2475156936 date "2016-07-11" @default.
• W2475156936 modified "2023-10-08" @default.
• W2475156936 title "Under-reported data analysis with INAR-hidden Markov chains" @default.
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• W2475156936 doi "https://doi.org/10.1002/sim.7026" @default.
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• W2475156936 hasPublicationYear "2016" @default.
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