Monte Carlo EM for Missing Covariates in Parametric Regression Models
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Publication:4666613
DOI10.1111/j.0006-341X.1999.00591.xzbMath1059.62662OpenAlexW2072742296WikidataQ52064992 ScholiaQ52064992MaRDI QIDQ4666613
Ming-Hui Chen, Joseph G. Ibrahim, Stuart R. Lipsitz
Publication date: 13 April 2005
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.0006-341x.1999.00591.x
EM algorithmmaximum likelihood estimationproportional hazardsGibbs samplerPoisson regressiongeneralized linear modelWeibull regressionmissing data mechanism
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Cites Work
- Using the EM-algorithm for survival data with incomplete categorical covariates
- Maximum likelihood estimation for mixed continuous and categorical data with missing values
- Inference and missing data
- Parameter Estimation from Incomplete Data in Binomial Regression When the Missing Data Mechanism is Nonignorable
- A conditional model for incomplete covariates in parametric regression models
- Adaptive Rejection Sampling for Gibbs Sampling
