Addressing misclassification for binary data: probit and t-link regressions

DOI10.1080/00949655.2013.787424zbMath1453.62598OpenAlexW1994899655MaRDI QIDQ5219485

María Jesús Rufo, Lizbeth Naranjo, Jacinto Martín, Carlos J. Pérez

Publication date: 12 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2013.787424

zbMATH Keywords

Markov chain Monte Carlo methodsgeneralized linear modelsdata augmentationexpectation-maximization algorithmBayesian methodsbinary regressionmisclassification

Mathematics Subject Classification ID

Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

Related Items (4)

A Bayesian approach for misclassified ordinal response data ⋮ The need to conduct repeated classifications in a logistic regression model with misclassification in the dependent variable ⋮ Repeated responses in misclassification binary regression: A Bayesian approach ⋮ Skewed link-based regression models for misclassified binary data

Uses Software

• boa

Cites Work

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