ArticlePsychometrika2022
Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework.
Article in Psychometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An introduction to the Item Response Warehouse (IRW): A resource for enhancing data usage in psychometrics.Behavior research methods · 2025Article
- Exact Exploratory Bi-factor Analysis: A Constraint-Based Optimization Approach.Psychometrika · 2025Article
- Exploratory General-Response Cognitive Diagnostic Models with Higher-Order Structures.Psychometrika · 2025Article
- Handling missing data in variational autoencoder based item response theory.The British journal of mathematical and statistical psychology · 2025Article
- Bayesian Adaptive Lasso for Detecting Item-Trait Relationship and Differential Item Functioning in Multidimensional Item Response Theory Models.Psychometrika · 2024Article
- Article
- Efficient Metropolis-Hastings Robbins-Monro Algorithm for High-Dimensional Diagnostic Classification Models.Applied psychological measurement · 2022Article
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Authors and funding
2 authors.
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Abstract
Latent variable models have been playing a central role in psychometrics and related fields. In many modern applications, the inference based on latent variable models involves one or several of the following features: (1) the presence of many latent variables, (2) the observed and latent variables being continuous, discrete, or a combination of both, (3) constraints on parameters, and (4) penalties on parameters to impose model parsimony. The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/or penalties on parameters. Solving this optimization problem is highly non-trivial, due to the complexities brought by the features mentioned above. Although several efficient algorithms have been proposed, there lacks a unified computational framework that takes all these features into account. In this paper, we fill the gap. Specifically, we provide a unified formulation for the optimization problem and then propose a quasi-Newton stochastic proximal algorithm. Theoretical properties of the proposed algorithms are established. The computational efficiency and robustness are shown by simulation studies under various settings for latent variable model estimation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.