Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models

5 Dec 2018Ioannis KosmidisDavid Firth

This paper studies the finiteness properties of the reduced-bias estimator for logistic regression that results from penalization of the likelihood by Jeffreys' invariant prior; and it provides geometric insights on the shrinkage towards equiprobability that the penalty induces. Some implications of finiteness and shrinkage for inference are discussed, particularly when inference is based on Wald-type procedures... (read more)

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