Highly Influenced

@inproceedings{Dunson2004ApproximateBI, title={Approximate Bayesian Inference for Quantiles}, author={David B. Dunson and Jack A. Taylor}, year={2004} }

- Published 2004

Suppose data consist of a random sample from a distribution function FY , which is unknown, and that interest focuses on inferences on θ, a vector of quantiles of FY . When the likelihood function is not fully specified, a posterior density cannot be calculated and Bayesian inference is difficult. This article considers an approach which relies on a substitution likelihood characterized by a vector of quantiles. Properties of the substitution likelihood are investigated, strategies for prior… CONTINUE READING