Default Priors for the Intercept Parameter in Logistic Regressions.

ABSTRACT: In logistic regression, separation occurs when a linear combination of predictors perfectly discriminates the binary outcome. Because finite-valued maximum likelihood parameter estimates do not exist under separation, Bayesian regressions with informative shrinkage of the regression coefficients offer a suitable alternative. Classical studies of separation imply that efficiency in estimating regression coefficients may also depend upon the choice of intercept prior, yet relatively little focus has been given on whether and how to shrink the intercept parameter. Alternative prior distributions for the intercept are proposed that downweight implausibly extreme regions of the parameter space, rendering regression estimates that are less sensitive to separation. Through simulation and the anal

SUBMITTER: Boonstra PS 

PROVIDER: S-EPMC6748335 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature