Marginal posterior simulation via higher-order tail area approximations

DOI10.1214/13-BA851zbMath1327.62159arXiv1212.1038OpenAlexW3102963602MaRDI QIDQ899009

Laura Ventura, Erlis Ruli, Nicola Sartori

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1212.1038

zbMATH Keywords

sensitivity analysisasymptotic expansionnuisance parameterMCMCBayesian computationmarginal posterior distributioninverse transform samplingmodified likelihood root

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Bayesian inference (62F15) Statistics of extreme values; tail inference (62G32) General nonlinear regression (62J02)

Related Items (6)

Can Bayesian, confidence distribution and frequentist inference agree? ⋮ Pseudo-Likelihoods for Bayesian Inference ⋮ On the validity of the formal Edgeworth expansion for posterior densities ⋮ Approximate Bayesian computation with modified log-likelihood ratios ⋮ Higher order asymptotic computation of Bayesian significance tests for precise null hypotheses in the presence of nuisance parameters ⋮ Higher-order Bayesian Approximations for Pseudo-posterior Distributions

Uses Software

• R
• BayesDA
• numDeriv

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Accurate parametric inference for small samples
• Asymptotics and the theory of inference
• Objective Bayesian higher-order asymptotics in models with nuisance parameters
• Adjustments of the profile likelihood from a new perspective
• Recent advances on Bayesian inference for \(P(X < Y)\)
• Posterior simulation via the signed root log-likelihood ratio
• An approximation to maximum likelihood estimates in reduced models
• Assessing Sensitivity to Priors Using Higher Order Approximations
• Accurate Approximations for Posterior Moments and Marginal Densities
• Noninformative priors for one parameter of many
• Approximate methods for assessing influence and sensitivity in Bayesian analysis
• Stable and invariant adjusted directed likelihoods
• Statistical Models
• Prior Distributions From Pseudo-Likelihoods in the Presence of Nuisance Parameters
• Applied Asymptotics

This page was built for publication: Marginal posterior simulation via higher-order tail area approximations