Bias correction in logistic regression with missing categorical covariates

DOI10.1016/j.jspi.2010.02.018zbMath1188.62203OpenAlexW2002261592MaRDI QIDQ974495

Ujjwal Das, Vivek Pradhan, Tapabrata Maiti

Publication date: 3 June 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2010.02.018

zbMATH Keywords

EM algorithmmaximum likelihood estimationbiasmissing at random

Mathematics Subject Classification ID

Point estimation (62F10) Generalized linear models (logistic models) (62J12)

Related Items

A Pseudo‐Bayesian Shrinkage Approach to Regression with Missing Covariates ⋮ Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

Cites Work

• Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information
• Moments and Distributions of Estimates of Population Parameters from Fragmentary Samples
• Bias reduction of maximum likelihood estimates
• Missing-Data Methods for Generalized Linear Models
• An invariant form for the prior probability in estimation problems
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