Learning latent and hierarchical structures in cognitive diagnosis models

DOI10.1007/s11336-022-09867-5zbMath1514.62305arXiv2104.02143OpenAlexW3149310895MaRDI QIDQ6160317

Chenchen Ma, Jing Ouyang, Gongjun Xu

Publication date: 23 June 2023

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.02143

zbMATH Keywords

hierarchical structurecognitive diagnosis modelpenalized EM algorithmQ-matrix

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (1)

A tensor-EM method for large-scale latent class analysis with binary responses

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Identifiability of diagnostic classification models
• A general method of empirical Q-matrix validation
• The generalized DINA model framework
• Defining a family of cognitive diagnosis models using log-linear models with latent variables
• Estimating the dimension of a model
• Bayesian estimation of the DINA \(Q\) matrix
• Regularized latent class analysis with application in cognitive diagnosis
• Convergence of a stochastic approximation version of the EM algorithm
• Identifiability of restricted latent class models with binary responses
• Learning large \(Q\)-matrix by restricted Boltzmann machines
• Hypothesis testing of the \(Q\)-matrix
• 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
• Hierarchical diagnostic classification models: a family of models for estimating and testing attribute hierarchies
• Hierarchical diagnostic classification models morphing into unidimensional `diagnostic' classification models -- a commentary
• Extended Bayesian information criteria for model selection with large model spaces
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Identifying Latent Structures in Restricted Latent Class Models
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• Likelihood-Based Selection and Sharp Parameter Estimation
• Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models
• Decomposition method with a variable parameter for a class of monotone variational inequality problems

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