Approximate Bayesian computation with modified log-likelihood ratios

Laura Ventura () and Nancy Reid ()

METRON, 2014, vol. 72, issue 2, 245 pages

Abstract: The aim of this contribution is to discuss approximate Bayesian computation based on the asymptotic theory of modified likelihood roots and log-likelihood ratios. Results on third-order approximations for univariate posterior distributions, also in the presence of nuisance parameters, are reviewed and the computation of asymptotic credible sets for a vector parameter of interest is illustrated. All these approximations are available at little additional computational cost over simple first-order approximations. Some illustrative examples are discussed, with particular attention to the use of matching priors. Copyright Sapienza Università di Roma 2014

Keywords: Bayesian simulation; Credible set; Higher-order asymptotics; Laplace approximation; Marginal posterior distribution; Matching priors; Modified likelihood root; Nuisance parameter; Pereira–Stern measure of evidence; Precise null hypothesis; Tail area probability (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s40300-014-0041-4

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