File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1080/00031305.2022.2141857
- Scopus: eid_2-s2.0-85143425816
- WOS: WOS:000910568800001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: RafterNet: Probabilistic Predictions in Multi-Response Regression
Title | RafterNet: Probabilistic Predictions in Multi-Response Regression |
---|---|
Authors | |
Keywords | Copulas Generative neural networks Learning distributions Multi-response regression Probabilistic forecasts Random forests |
Issue Date | 2022 |
Citation | American Statistician, 2022 How to Cite? |
Abstract | A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced. Random forests are used as marginal models for each response variable and, as novel contribution of the present work, the dependence between the multiple response variables is modeled by a generative neural network. This combined modeling approach of random forests, corresponding empirical marginal residual distributions and a generative neural network is referred to as RafterNet. Multiple datasets serve as examples to demonstrate the flexibility of the approach and its impact for making probabilistic forecasts. |
Persistent Identifier | http://hdl.handle.net/10722/325587 |
ISSN | 2023 Impact Factor: 1.8 2023 SCImago Journal Rankings: 0.675 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hofert, Marius | - |
dc.contributor.author | Prasad, Avinash | - |
dc.contributor.author | Zhu, Mu | - |
dc.date.accessioned | 2023-02-27T07:34:34Z | - |
dc.date.available | 2023-02-27T07:34:34Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | American Statistician, 2022 | - |
dc.identifier.issn | 0003-1305 | - |
dc.identifier.uri | http://hdl.handle.net/10722/325587 | - |
dc.description.abstract | A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced. Random forests are used as marginal models for each response variable and, as novel contribution of the present work, the dependence between the multiple response variables is modeled by a generative neural network. This combined modeling approach of random forests, corresponding empirical marginal residual distributions and a generative neural network is referred to as RafterNet. Multiple datasets serve as examples to demonstrate the flexibility of the approach and its impact for making probabilistic forecasts. | - |
dc.language | eng | - |
dc.relation.ispartof | American Statistician | - |
dc.subject | Copulas | - |
dc.subject | Generative neural networks | - |
dc.subject | Learning distributions | - |
dc.subject | Multi-response regression | - |
dc.subject | Probabilistic forecasts | - |
dc.subject | Random forests | - |
dc.title | RafterNet: Probabilistic Predictions in Multi-Response Regression | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/00031305.2022.2141857 | - |
dc.identifier.scopus | eid_2-s2.0-85143425816 | - |
dc.identifier.eissn | 1537-2731 | - |
dc.identifier.isi | WOS:000910568800001 | - |