Efficient and accurate approximate Bayesian inference with an application to insurance data

Efficient and accurate Bayesian Markov chain Monte Carlo methodology is proposed for the estimation of event rates under an overdispersed Poisson distribution. An approximate Gibbs sampling method and an exact independence-type Metropolis–Hastings algorithm are derived, based on a log-normal/gamma m...

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Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 52; no. 5; pp. 2604 - 2622
Main Authors:	Streftaris, George, Worton, Bruce J.
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 20.01.2008 Elsevier Science Elsevier
Series:	Computational Statistics & Data Analysis
Subjects:	Bayes risk Distribution theory Effective sample size Entropy distance Exact sciences and technology General topics Hierarchical Bayesian analysis Insurance claims Markov chain Monte Carlo Mathematics Mixture distribution Monte Carlo error Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Sciences and techniques of general use Statistics Bayes risk Hierarchical Bayesian analysis Monte Carlo error Effective sample size Insurance claims Markov chain Monte Carlo Mixture distribution Entropy distance Density estimation Conditional distribution Mixing Divergence Statistical distribution Estimator robustness Gaussian distribution Entropy Stochastic method Statistical simulation Markov chain Loss function Distribution function Approximation theory Conditional moment Bayes estimation Mixed distribution Metropolis algorithm Monte Carlo method Data analysis Approximate method Statistical moment Finance Metropolis Hastings algorithm Independence Statistical estimation Moment matching Gibbs sampling Poisson distribution Gamma distribution Statistical method Estimating function Numerical analysis Statistical computation Insurance
ISSN:	0167-9473, 1872-7352
Online Access:	Get full text
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