Inferring the number of attributes for the exploratory DINA model

DOI10.1007/s11336-021-09750-9zbMath1476.62244OpenAlexW3136043718MaRDI QIDQ823856

N. E. Zubov

Publication date: 16 December 2021

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-021-09750-9

zbMATH Keywords

dimensionalityDirichlet processesBayesianreversible jump MCMCIndian buffet processdiagnostic models

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (7)

Variational Bayes inference for hidden Markov diagnostic classification models ⋮ Data‐driven Q‐matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment ⋮ Generic identifiability of the DINA model and blessing of latent dependence ⋮ A note on weaker conditions for identifying restricted latent class models for binary responses ⋮ Identifiability of hidden Markov models for learning trajectories in cognitive diagnosis ⋮ Bayesian inference for an unknown number of attributes in restricted latent class models ⋮ Exploratory restricted latent class models with monotonicity requirements under Pòlya-gamma data augmentation

Cites Work

• Unnamed Item
• Unnamed Item
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Identifiability of diagnostic classification models
• A tutorial on Bayesian nonparametric models
• The generalized DINA model framework
• Model evaluation and multiple strategies in cognitive diagnosis: an analysis of fraction subtraction data
• Defining a family of cognitive diagnosis models using log-linear models with latent variables
• Bayesian estimation of the DINA \(Q\) matrix
• Identifiability of restricted latent class models with binary responses
• An exploratory diagnostic model for ordinal responses with binary attributes: identifiability and estimation
• A sparse latent class model for cognitive diagnosis
• A constrained Metropolis-Hastings Robbins-Monro algorithm for \(\boldsymbol{Q}\) matrix estimation in DINA models
• Higher-order latent trait models for cognitive diagnosis
• Assessing growth in a diagnostic classification model framework
• On the identifiability of diagnostic classification models
• Estimating the cognitive diagnosis \(Q\) matrix with expert knowledge: application to the fraction-subtraction dataset
• The sufficient and necessary condition for the identifiability and estimability of the DINA Model
• Restricted Indian buffet processes
• Theory of self-learning \(Q\)-matrix
• Data Analytic Methods for Latent Partially Ordered Classification Models
• The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem
• Identifying Latent Structures in Restricted Latent Class Models
• Sequential detection of learning in cognitive diagnosis
• Sufficient and Necessary Conditions for the Identifiability of the $Q$-matrix
• Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models

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