RafterNet: Probabilistic Predictions in Multi-Response Regression.
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 mod...
| Publicado en: | American Statistician Vol. 77; no. 4; pp. 406 - 417 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Nov2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=173367637&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 173367637 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Nov2023 vid: 77 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 173367637 10.1080/00031305.2022.2141857 ppf: 406 ppct: 11 formats: tig: atl: RafterNet: Probabilistic Predictions in Multi-Response Regression. aug: au: Hofert, Marius Prasad, Avinash Zhu, Mu affil: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada su: Forecasting Marginal distributions Random forest algorithms sug: subj: Forecasting Marginal distributions Random forest algorithms keyword: Copulas Generative neural networks Learning distributions Multi-response regression Probabilistic forecasts Random forests Copulas Generative neural networks Learning distributions Multi-response regression Probabilistic forecasts Random forests ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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