Global identifiability of latent class models with applications to diagnostic test accuracy studies: A Grobner basis approach.

ABSTRACT: Identifiability of statistical models is a fundamental regularity condition that is required for valid statistical inference. Investigation of model identifiability is mathematically challenging for complex models such as latent class models. Jones et al. used Goodman's technique to investigate the identifiability of latent class models with applications to diagnostic tests in the absence of a gold standard test. The tool they used was based on examining the singularity of the Jacobian or the Fisher information matrix, in order to obtain insights into local identifiability (ie, there exists a neighborhood of a parameter such that no other parameter in the neighborhood leads to the same probability distribution as the parameter). In this paper, we investigate a stronger condition: global id

SUBMITTER: Duan R 

PROVIDER: S-EPMC7036323 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature