Bias Reduction and a Solution for Separation of Logistic Regression with Missing Covariates

DOI10.1111/j.1541-0420.2008.01186.xzbMath1180.62102OpenAlexW2110705358WikidataQ37855036 ScholiaQ37855036MaRDI QIDQ5850976

Vivek Pradhan, Tapabrata Maiti

Publication date: 21 January 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2008.01186.x

zbMATH Keywords

EM algorithmbiasmaximum likelihood estimatesmall sampleinterferoncancer trial

Mathematics Subject Classification ID

Related Items (2)

Corrected likelihood-ratio tests in logistic regression using small-sample data ⋮ Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

Uses Software

• SAS
• LogXact

Cites Work

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• On the existence of maximum likelihood estimates in logistic regression models
• Computing Distributions for Exact Logistic Regression
• 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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