Goodness of fit for models with intractable likelihood

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dc.contributor.author Cabras, Stefano
dc.contributor.author Castellanos, María Eugenia
dc.contributor.author Ratmann, Oliver
dc.date.accessioned 2021-11-25T19:06:49Z
dc.date.issued 2021-09-01
dc.identifier.bibliographicCitation Cabras, S., Castellanos, M. E., & Ratmann, O. (2021). Goodness of fit for models with intractable likelihood.TEST,30 (3), pp. 713-736
dc.identifier.issn 1133-0686
dc.identifier.uri http://hdl.handle.net/10016/33692
dc.description.abstract Routine goodness-of-fit analyses of complex models with intractable likelihoods are hampered by a lack of computationally tractable diagnostic measures with wellunderstood frequency properties, that is, with a known sampling distribution. This frustrates the ability to assess the extremity of the data relative to fitted simulation models in terms of pre-specified test statistics, an essential requirement for model improvement. Given an Approximate Bayesian Computation setting for a posited model with an intractable likelihood for which it is possible to simulate from them, we present a general and computationally inexpensive Monte Carlo framework for obtaining p-valuesthat are asymptotically uniformly distributed in [0, 1] under the posited model when assumptions about the asymptotic equivalence between the conditional statistic and the maximum likelihood estimator hold. The proposed framework follows almost directly from the conditional predictive p-value proposed in the Bayesian literature. Numerical investigations demonstrate favorable power properties in detecting actual model discrepancies relative to other diagnostic approaches. We illustrate the technique on analytically tractable examples and on a complex tuberculosis transmission model.
dc.description.sponsorship Authors have been founded by MINECO-Spain projects PID2019-104790GB-I00 (M.E. Castellanos and S. Cabras) and Wellcome Trust fellowship WR092311MF (O. Ratmann).
dc.language.iso eng
dc.publisher Springer
dc.rights © Sociedad de Estadística e Investigación Operativa
dc.subject.other Approximate bayesian computation
dc.subject.other Model adequacy
dc.subject.other Model checking
dc.subject.other Simulation-based modeling
dc.title Goodness of fit for models with intractable likelihood
dc.type article
dc.subject.eciencia Estadística
dc.identifier.doi https://doi.org/10.1007/s11749-020-00747-7
dc.rights.accessRights embargoedAccess
dc.relation.projectID Gobierno de España. PID2019-104790GB-I00
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 713
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 736
dc.identifier.publicationtitle TEST
dc.identifier.publicationvolume 30
dc.identifier.uxxi AR/0000027614
carlosiii.embargo.liftdate 2022-09
carlosiii.embargo.terms 2022-09
dc.contributor.funder Ministerio de Asuntos Económicos y Transformación Digital
dc.contributor.funder
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