Joint regression modeling for missing categorical covariates in generalized linear models

DOI10.1080/02664763.2018.1438376OpenAlexW2792180307MaRDI QIDQ5036347

Gabriel Escarela, Luis Carlos Pérez-Ruíz

Publication date: 23 February 2022

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

Full work available at URL: https://doi.org/10.1080/02664763.2018.1438376

zbMATH Keywords

copulavinesEM algorithmmissing datamultivariate distributionpair copula constructions

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• copula
• MICE
• copula

Cites Work

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• Pair-copula constructions of multiple dependence
• Vines -- a new graphical model for dependent random variables.
• Probability density decomposition for conditionally dependent random variables modeled by vines
• Inference and missing data
• Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-ignorable
• A conditional model for incomplete covariates in parametric regression models
• Multivariate Dispersion Models Generated From Gaussian Copula
• Pair Copula Constructions for Multivariate Discrete Data
• Missing-Data Methods for Generalized Linear Models

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