Identifiability of restricted latent class models with binary responses

By Gongjun Xu. 2016

105 citations recorded by the source corpus.

Statistical latent class models are widely used in social and psychological researches, yet it is often difficult to establish the identifiability of the model parameters. In this paper we consider the identifiability issue of a family of restricted latent class models, where the restriction structures are needed to reflect pre-specified assumptions on the related assessment. We establish the identifiability results in the strict sense and specify which types of restriction structure would give the identifiability of the model parameters. The results not only guarantee the validity of many of the popularly used models, but also provide a guideline for the related experimental design, where in the current applications the design is usually experience based and identifiability is not guaranteed. Theoretically, we develop a new technique to establish the identifiability result, which may be extended to other restricted latent class models.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6ba38d1fa241a8014c82f057ee1f8855dacba59d

Citations in the corpus

• Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “Efforts have been made to address these questions; see Chen et al. (2015), Chen et al. (2020a), Chiu et al. (2009), Fang et al. (2019), Fang et al. (2021), Gu and Xu (2020), Liu et al. (2013), Xu and Zhang (2016), Xu (2017), and Xu and Shang (2018).”