R-squared for Bayesian regression models
| dc.contributor | Aalto-yliopisto | fi |
| dc.contributor | Aalto University | en |
| dc.contributor.author | Gelman, Andrew | en_US |
| dc.contributor.author | Goodrich, Ben | en_US |
| dc.contributor.author | Gabry, Jonah | en_US |
| dc.contributor.author | Vehtari, Aki | en_US |
| dc.contributor.department | Department of Computer Science | en |
| dc.contributor.groupauthor | Professorship Vehtari Aki | en |
| dc.contributor.groupauthor | Helsinki Institute for Information Technology (HIIT) | en |
| dc.contributor.groupauthor | Probabilistic Machine Learning | en |
| dc.contributor.organization | Columbia University | en_US |
| dc.date.accessioned | 2019-06-20T13:16:43Z | |
| dc.date.available | 2019-06-20T13:16:43Z | |
| dc.date.embargo | info:eu-repo/date/embargoEnd/2020-05-13 | en_US |
| dc.date.issued | 2019-01-01 | en_US |
| dc.description.abstract | The usual definition of R 2 (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an alternative definition similar to one that has appeared in the survival analysis literature: the variance of the predicted values divided by the variance of predicted values plus the expected variance of the errors. | en |
| dc.description.version | Peer reviewed | en |
| dc.format.extent | 6 | |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Gelman, A, Goodrich, B, Gabry, J & Vehtari, A 2019, 'R-squared for Bayesian regression models', American Statistician, pp. 1-6. https://doi.org/10.1080/00031305.2018.1549100 | en |
| dc.identifier.doi | 10.1080/00031305.2018.1549100 | en_US |
| dc.identifier.issn | 0003-1305 | |
| dc.identifier.issn | 1537-2731 | |
| dc.identifier.other | PURE UUID: d31122a0-cec7-477e-a87e-f6ec2e6ab8ae | en_US |
| dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/d31122a0-cec7-477e-a87e-f6ec2e6ab8ae | en_US |
| dc.identifier.other | PURE LINK: https://doi.org/10.1080/00031305.2018.1549100 | |
| dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/34206843/bayes_R2_v3.pdf | |
| dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/38878 | |
| dc.identifier.urn | URN:NBN:fi:aalto-201906203944 | |
| dc.language.iso | en | en |
| dc.publisher | Taylor & Francis | |
| dc.relation.ispartofseries | American Statistician | en |
| dc.relation.ispartofseries | pp. 1-6 | en |
| dc.rights | openAccess | en |
| dc.subject.keyword | Bayesian methods | en_US |
| dc.subject.keyword | R-squared | en_US |
| dc.subject.keyword | Regression | en_US |
| dc.title | R-squared for Bayesian regression models | en |
| dc.type | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä | fi |
| dc.type.version | acceptedVersion |
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