Abstract
Hidden Markov models (HMMs) offer an attractive way of accounting and correcting for measurement error in longitudinal data as they do not require the use of a ‘gold standard’ data source as a benchmark. However, while the standard HMM assumes the errors to be independent or random, some common situations
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