Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications

DOI10.1080/01621459.2019.1635485zbMath1452.62407arXiv1712.08966OpenAlexW2963639289WikidataQ114641967 ScholiaQ114641967MaRDI QIDQ5146028

Xiaoou Li, Siliang Zhang, Yunxiao Chen

Publication date: 22 January 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1712.08966

zbMATH Keywords

confirmatory factor analysishigh-dimensional latent factor modelidentifiability of latent factorslarge-scale psychological measurementstructured low-rank matrix

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12)

Related Items (13)

Multidimensional item response theory in the style of collaborative filtering ⋮ Identifiability of Bifactor Models ⋮ A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis ⋮ Unnamed Item ⋮ A Gibbs‐INLA algorithm for multidimensional graded response model analysis ⋮ Rotation to sparse loadings using \(L^p\) losses and related inference problems ⋮ A tensor-EM method for large-scale latent class analysis with binary responses ⋮ A note on exploratory item factor analysis by singular value decomposition ⋮ A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso ⋮ A note on identifiability conditions in confirmatory factor analysis ⋮ The Blessings of Multiple Causes ⋮ A continuous-time dynamic choice measurement model for problem-solving process data ⋮ Computation for latent variable model estimation: a unified stochastic proximal framework

Uses Software

• LowRankModels
• Pyglrm

Cites Work

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