Approximate Bayesian computation using asymptotically normal point estimates
From MaRDI portal
Publication:6136276
DOI10.1007/s00180-022-01226-3OpenAlexW4224947489MaRDI QIDQ6136276
Publication date: 29 August 2023
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00180-022-01226-3
bootstrapBayesian analysisasymptotic normalitypoint estimationintractable likelihoodlikelihood-free methods
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bayesian Synthetic Likelihood
- Approximate Bayesian computational methods
- Bayesianly justifiable and relevant frequency calculations for the applied statistician
- On limiting posterior distributions
- Consistent and robust Bayes procedures for location based on partial information
- A note on minimum distance estimates
- Consistency of maximum likelihood and Bayes estimates
- Estimation of the non-centrality parameter of a chi squared distribution
- Asymptotics in statistics: some basic concepts
- Robust covariance estimates based on resampling
- Likelihood-based inference with singular information matrix
- An approximate likelihood perspective on ABC methods
- Likelihood-free Bayesian estimation of multivariate quantile distributions
- Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models
- Recalibration: a post-processing method for approximate Bayesian computation
- Posterior convergence given the mean
- The Bernstein-von Mises theorem under misspecification
- Exponential-family models of random graphs: inference in finite, super and infinite population scenarios
- New insights into approximate Bayesian computation
- Risk of Bayesian Inference in Misspecified Models, and the Sandwich Covariance Matrix
- Science and Statistics
- Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
- Asymptotic Statistics
- Asymptotic Distribution of P Values in Composite Null Models
- Asymptotic properties of approximate Bayesian computation
- Robust Statistics
- Asymptotic normality of the posterior given a statistic
- Interpreting Statistical Evidence by using Imperfect Models: Robust Adjusted Likelihood Functions
- Componentwise approximate Bayesian computation via Gibbs-like steps
- On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
- Handbook of Approximate Bayesian Computation
- On the asymptotic efficiency of approximate Bayesian computation estimators
- Sequential Monte Carlo without likelihoods
- On Bayes procedures
- Some contributions to the asymptotic theory of Bayes solutions
- An adaptive Metropolis algorithm
