Please use this identifier to cite or link to this item: https://doi.org/10.1214/16-BA1033
DC FieldValue
dc.titleApproximation of Bayesian predictive p-values with regression ABC
dc.contributor.authorNott, D.J.
dc.contributor.authorDrovandi, C.C.
dc.contributor.authorMengersen, K.
dc.contributor.authorEvans, M.
dc.date.accessioned2021-12-09T05:04:32Z
dc.date.available2021-12-09T05:04:32Z
dc.date.issued2018
dc.identifier.citationNott, D.J., Drovandi, C.C., Mengersen, K., Evans, M. (2018). Approximation of Bayesian predictive p-values with regression ABC. Bayesian Analysis 13 (1) : 59-83. ScholarBank@NUS Repository. https://doi.org/10.1214/16-BA1033
dc.identifier.issn1936-0975
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/210131
dc.description.abstractIn the Bayesian framework a standard approach to model criticism is to compare some function of the observed data to a reference predictive distribution. The result of the comparison can be summarized in the form of a p-value, and computation of some kinds of Bayesian predictive p-values can be challenging. The use of regression adjustment approximate Bayesian computation (ABC) methods is explored for this task. Two problems are considered. The first is approximation of distributions of prior predictive p-values for the purpose of choosing weakly informative priors in the case where the model checking statistic is expensive to compute. Here the computation is difficult because of the need to repeatedly sample from a prior predictive distribution for different values of a prior hyperparameter. The second problem considered is the calibration of posterior predictive p-values so that they are uniformly distributed under some reference distribution for the data. Computation is difficult because the calibration process requires repeated approximation of the posterior for different data sets under the reference distribution. In both these problems we argue that high accuracy in the computations is not required, which makes fast approximations such as regression adjustment ABC very useful. We illustrate our methods with several examples. © 2018 International Society for Bayesian Analysis.
dc.publisherInternational Society for Bayesian Analysis
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2018
dc.subjectABC
dc.subjectBayesian inference
dc.subjectBayesian p-values
dc.subjectPosterior predictive check
dc.subjectPrior predictive check
dc.subjectWeakly informative prior
dc.typeArticle
dc.contributor.departmentDEPT OF STATISTICS & APPLIED PROBABILITY
dc.description.doi10.1214/16-BA1033
dc.description.sourcetitleBayesian Analysis
dc.description.volume13
dc.description.issue1
dc.description.page59-83
Appears in Collections:Staff Publications
Elements

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1214_16-BA1033.pdf1.19 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

11
checked on Nov 24, 2022

Page view(s)

69
checked on Nov 17, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons