Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

DOI10.1080/00031305.2017.1407359OpenAlexW2780001753MaRDI QIDQ5868209

Arnab Maity, Ujjwal Das, Vivek Pradhan

Publication date: 20 September 2022

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2017.1407359

zbMATH Keywords

EM algorithmseparationnonignorable missingfirth correctionmissing binary response

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Assessing selection bias in regression coefficients estimated from nonprobability samples with applications to genetics and demographic surveys ⋮ On classification with nonignorable missing data ⋮ On the maximal deviation of kernel regression estimators with NMAR response variables ⋮ Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles

Uses Software

• R
• MICE
• mi

Cites Work

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• Bias reduction in exponential family nonlinear models
• Bias correction in logistic regression with missing categorical covariates
• A generic algorithm for reducing bias in parametric estimation
• Multiple Imputation in Mixture Models for Nonignorable Nonresponse With Follow-ups
• Multinomial logit bias reduction via the Poisson log-linear model
• On the existence of maximum likelihood estimates in logistic regression models
• On bias reduction in exponential and non-exponential family regression models
• Bias Reduction using Stochastic Approximation
• Parameter Estimation from Incomplete Data in Binomial Regression When the Missing Data Mechanism is Nonignorable
• Bias reduction of maximum likelihood estimates
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• Testing the proportional odds assumption in multiply imputed ordinal longitudinal data
• An invariant form for the prior probability in estimation problems
• Bias Reduction and a Solution for Separation of Logistic Regression with Missing Covariates

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