Approximate Bayesian Inference for Quantiles

  title={Approximate Bayesian Inference for Quantiles},
  author={David B. Dunson and Jack A. Taylor},
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