Abstract:
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Finite mixture models provide a flexible framework to study unobserved entities and have arisen in many statistical applications. The flexibility of these models in adapting various complicated structures makes it crucial to establish model identifiability when applying them in practice to ensure study validity and interpretation. However, researches to establish the identifiability of finite mixture model are limited, especially when covariates are involved. In addition, most efforts have focused on finding conditions for local identifiability while global identifiability was rarely discussed. In this talk, we will discuss the different role of local and global identifiability, as well as the approaches for establishing local and global identifiability. The finite mixture model we considered here is constructed for estimating diagnostic error rate without a gold standard, which allows for continuous, discrete or mix-typed manifest variables, ordinal or nominal latent groups, and flexible inclusion of covariates. We also provide intuitive explanation of the conditions and discuss the effect of including covariates in the model.
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