| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1151689 | Statistics & Probability Letters | 2013 | 6 Pages |
Abstract
Higher-order asymptotic arguments for a scalar parameter of interest have been widely investigated for Bayesian inference. In this paper the theory of asymptotic expansions is discussed for a vector parameter of interest. A modified loglikelihood ratio is suggested, which can be used to derive approximate Bayesian credible sets with accurate frequentist coverage. Three examples are illustrated.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Laura Ventura, Erlis Ruli, Walter Racugno,