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.
Rosychuk,R J and Thompson,& E . (2022). Parameter Identifiability Issues in a Latent Ma- rkov Model for Misclassified Binary Responses. Journal of the Iranian Statistical Society, 3(1), 39-57.
MLA
Rosychuk,R J , and Thompson,& E . "Parameter Identifiability Issues in a Latent Ma- rkov Model for Misclassified Binary Responses", Journal of the Iranian Statistical Society, 3, 1, 2022, 39-57.
HARVARD
Rosychuk R J, Thompson & E. (2022). 'Parameter Identifiability Issues in a Latent Ma- rkov Model for Misclassified Binary Responses', Journal of the Iranian Statistical Society, 3(1), pp. 39-57.
CHICAGO
R J Rosychuk and & E Thompson, "Parameter Identifiability Issues in a Latent Ma- rkov Model for Misclassified Binary Responses," Journal of the Iranian Statistical Society, 3 1 (2022): 39-57,
VANCOUVER
Rosychuk R J, Thompson & E. Parameter Identifiability Issues in a Latent Ma- rkov Model for Misclassified Binary Responses. JIRSS. 2022;3(1):39-57.
Journal of the Iranian Statistical Society