Incorporating conditional dependence in latent class models for probabilistic record linkage: does it matter?

DOI10.1214/19-AOAS1256zbMath1433.62319OpenAlexW2980449450WikidataQ106577208 ScholiaQ106577208MaRDI QIDQ2281221

Huiping Xu, Shaun Grannis, Xiaochun Li, Siu L. Hui, Changyu Shen

Publication date: 19 December 2019

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

Full work available at URL: https://projecteuclid.org/euclid.aoas/1571277772

zbMATH Keywords

log-linear modelfinite mixturelatent class analysisconditional dependencerecord linkageGaussian random effects model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Order statistics; empirical distribution functions (62G30)

Related Items (4)

Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system ⋮ Improving Probabilistic Record Linkage Using Statistical Prediction Models ⋮ Incorporating conditional dependence in latent class models for probabilistic record linkage: does it matter? ⋮ A simple two-step procedure using the Fellegi–Sunter model for frequency-based record linkage

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