The empirical likelihood prior applied to bias reduction of general estimating equations.

ABSTRACT: The practice of employing empirical likelihood (EL) components in place of parametric likelihood functions in the construction of Bayesian-type procedures has been well-addressed in the modern statistical literature. The EL prior, a Jeffreys-type prior, which asymptotically maximizes the Shannon mutual information between data and the parameters of interest, is rigorously derived. The focus of the proposed approach is on an integrated Kullback-Leibler distance between the EL-based posterior and prior density functions. The EL prior density is the density function for which the corresponding posterior form is asymptotically negligibly different from the EL. The proposed result can be used to develop a methodology for reducing the asymptotic bias of solutions of general estimating equations

SUBMITTER: Vexler A 

PROVIDER: S-EPMC6481956 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature