Parameter Identifiability Issues in a Latent Ma- rkov Model for Misclassified Binary Responses
نویسندگان
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چکیده
Medical researchers may be interested in disease processes that are not directly observable. Imperfect diagnostic tests may be used repeatedly to monitor the condition of a patient in the absence of a gold standard. We consider parameter identifiability and estimability in a Markov model for alternating binary longitudinal responses that may be misclassified. Exactly two distinct sets of parameter values are shown to generate the distribution for the data in a common situation and we propose a restriction to distinguishes the two. Even with the restriction, parameters may not be estimable. Issues of sampling and correct model specification are discussed.