Bayesian estimation and model selection in ordered latent class models for polytomous items

Psychometrika ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 519-538 ◽  
Author(s):  
M. J. H. van Onna
Keyword(s):  
Model Selection ◽  
Bayesian Estimation ◽  
Latent Class ◽  
Latent Class Models ◽  
Polytomous Items ◽  
Class Models

scholarly journals Effective Dimensions of Hierarchical Latent Class Models

2004 ◽  
Vol 21 ◽  
pp. 1-17 ◽  
Author(s):  
N. L. Zhang ◽  
T. Kocka
Keyword(s):  
Model Selection ◽  
Bayesian Networks ◽  
Latent Class ◽  
Empirical Studies ◽  
Latent Class Models ◽  
Penalty Term ◽  
Tree Models ◽  
Effective Dimensions ◽  
Class Models ◽  
Model Dimension

Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are latent. There are no theoretically well justified model selection criteria for HLC models in particular and Bayesian networks with latent nodes in general. Nonetheless, empirical studies suggest that the BIC score is a reasonable criterion to use in practice for learning HLC models. Empirical studies also suggest that sometimes model selection can be improved if standard model dimension is replaced with effective model dimension in the penalty term of the BIC score. Effective dimensions are difficult to compute. In this paper, we prove a theorem that relates the effective dimension of an HLC model to the effective dimensions of a number of latent class models. The theorem makes it computationally feasible to compute the effective dimensions of large HLC models. The theorem can also be used to compute the effective dimensions of general tree models.

Model Selection Information Criteria for Non-Nested Latent Class Models

1997 ◽  
Vol 22 (3) ◽  
pp. 249-264 ◽  
Author(s):  
Ting Hsiang Lin ◽  
C. Mitchell Dayton
Keyword(s):  
Model Selection ◽  
Latent Class ◽  
Error Rates ◽  
Latent Class Models ◽  
Information Criteria ◽  
Behavioral Analysis ◽  
Nested Models ◽  
Complex Models ◽  
Class Models ◽  
Simple Models

Latent class models have been developed for assessment of hierarchic relations in scaling and behavioral analysis. This article investigated the use of three model selection information criteria—Akaike AIC, Schwarz SIC, and Bozdogan CAIC—for non-nested models. In general, SIC and CAIC were superior to AIC for relatively simple models, whereas AIC was superior for more complex models, although accuracy was often quite low for such models. In addition, some effects were detected for error rates in the models.

On the robustness of latent class models for diagnostic testing with no gold standard

2021 ◽  
Author(s):  
Matthew R. Schofield ◽  
Michael J. Maze ◽  
John A. Crump ◽  
Matthew P. Rubach ◽  
Renee Galloway ◽  
...  
Keyword(s):  
Gold Standard ◽  
Latent Class ◽  
Diagnostic Testing ◽  
Latent Class Models ◽  
No Gold Standard ◽  
Class Models

STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models

2017 ◽  
Vol 138 ◽  
pp. 37-47 ◽  
Author(s):  
Polychronis Kostoulas ◽  
Søren S. Nielsen ◽  
Adam J. Branscum ◽  
Wesley O. Johnson ◽  
Nandini Dendukuri ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Latent Class ◽  
Latent Class Models ◽  
Class Models
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