A fast Monte Carlo expectation–maximization algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with atypical glandular cells

DOI10.1080/02664763.2013.825704OpenAlexW1991476145WikidataQ37251240 ScholiaQ37251240MaRDI QIDQ5129151

Randy L. Carter, Shu-Yuan Liao, Kathleen Darcy, James Kauderer, Le Kang

Publication date: 26 October 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2013.825704

zbMATH Keywords

latent class modeldiagnostic accuracyimperfect gold standardbootstrap standard errorsadjusted information matrixMCEM estimation

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• bootstrap

Cites Work

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• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Numerical Optimization
• Maximum Likelihood Variance Components Estimation for Binary Data
• Latent Variable Modeling of Diagnostic Accuracy
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• Latent Variable Regression for Multiple Discrete Outcomes
• An automated (Markov chain) Monte Carlo EM algorithm
• A Probit Latent Class Model with General Correlation Structures for Evaluating Accuracy of Diagnostic Tests

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