A Pólya–Gamma sampler for a generalized logistic regression
dc.contributor.author | Dalla Valle, Luciana | |
dc.contributor.author | Leisen, Fabrizio | |
dc.contributor.author | Rossini, Luca | |
dc.contributor.author | Zhu, W | |
dc.date.accessioned | 2019-11-15T10:53:01Z | |
dc.date.available | 2019-11-15T10:53:01Z | |
dc.date.issued | 2021-09-22 | |
dc.identifier.issn | 0094-9655 | |
dc.identifier.issn | 1563-5163 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/15149 | |
dc.description | Revised Version of the paper | |
dc.description.abstract |
In this paper, we introduce a novel Bayesian data augmentation approach for estimating the parameters of the generalized logistic regression model. We propose a Pólya–Gamma sampler algorithm that allows us to sample from the exact posterior distribution, rather than relying on approximations. A simulation study illustrates the flexibility and accuracy of the proposed approach to capture heavy and light tails in binary response data of different dimensions. The algorithm performance is tested on simulated data. Furthermore, the methodology is applied to two different real datasets, where we demonstrate that the Pólya–Gamma sampler provides more precise estimates than the empirical likelihood method, outperforming approximate approaches. | |
dc.format.extent | 2899-2916 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Informa UK Limited | |
dc.subject | Bayesian inference | |
dc.subject | generalized logistic regression | |
dc.subject | Pó | |
dc.subject | lya– | |
dc.subject | Gamma sampler | |
dc.subject | recidivism data | |
dc.title | A Pólya–Gamma sampler for a generalized logistic regression | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000639077700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 14 | |
plymouth.volume | 91 | |
plymouth.publication-status | Published | |
plymouth.journal | Journal of Statistical Computation and Simulation | |
dc.identifier.doi | 10.1080/00949655.2021.1910947 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.identifier.eissn | 1563-5163 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1080/00949655.2021.1910947 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |