Show simple item record

dc.contributor.authorFaisal, Muhammad*
dc.contributor.authorFutschik, A.*
dc.contributor.authorHussain, I.*
dc.contributor.authorAbd-el.Moemen, M.*
dc.date.accessioned2018-02-02T10:43:04Z
dc.date.available2018-02-02T10:43:04Z
dc.date.issued2016
dc.identifier.citationFaisal M, Futschik A, Hussain I et al (2016) Choosing summary statistics by least angle regression for approximate Bayesian computation. Journal of Applied Statistics. 43(12): 2191-2202.en_US
dc.identifier.urihttp://hdl.handle.net/10454/14801
dc.descriptionYesen_US
dc.description.abstractBayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.en_US
dc.description.sponsorshipHigher Education Commission (HEC), College Deanship of Scientific Research, King Saud University, Riyadh Saudi Arabia - research group project RGP-VPP-280.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1080/02664763.2015.1134447en_US
dc.rights© 2016 Taylor & Francis. This is an Author's Original Manuscript of an article published by Taylor & Francis in the Journal of Applied Statistics on 01 Feb 2016 available online at http://www.tandfonline.com/10.1080/02664763.2015.1134447en_US
dc.subjectLikelihood-free methods; Least angle regression; Mutation; Population genetics; Recombinationen_US
dc.titleChoosing summary statistics by least angle regression for approximate Bayesian computationen_US
dc.status.refereedYesen_US
dc.date.Accepted2015-12-16
dc.date.application2016-02-01
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-07-28T02:55:10Z


Item file(s)

Thumbnail
Name:
Faisal_Journal_of_Applied_Stat ...
Size:
618.4Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record