Bias Reduction in Binomial-Response Generalized Linear Models

Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.


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0.7.2 by Ioannis Kosmidis, 9 months ago

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Authors: Ioannis Kosmidis [aut, cre]

Documentation:   PDF Manual  

Task views: Econometrics, Statistics for the Social Sciences

GPL (>= 2) license

Depends on profileModel

Suggests MASS

Imported by BradleyTerry2, MixedPsy, analogue, brlrmr.

Depended on by glmvsd.

Suggested by abn, enrichwith, mbrglm, optmatch, picante.

Enhanced by prediction, stargazer.

See at CRAN