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dc.contributor.authorLi, Rui
dc.contributor.authorReich, Brian J.
dc.contributor.authorBondell, Howard D.
dc.date.accessioned2021-02-24T06:38:36Z
dc.date.available2021-02-24T06:38:36Z
dc.date.issued2021-02-22
dc.identifier.citationLi, R., Reich, B. J., & Bondell, H. D. (2021). Deep distribution regression. Computational Statistics & Data Analysis, 107203. doi:10.1016/j.csda.2021.107203
dc.identifier.issn0167-9473
dc.identifier.doi10.1016/j.csda.2021.107203
dc.identifier.urihttp://hdl.handle.net/10754/667632
dc.description.abstractDue to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles of the target quantity and do not provide the full conditional distribution, which contains uncertainty information that might be crucial for decision making. A general solution consists of transforming a conditional distribution estimation problem into a constrained multi-class classification problem, in which tools such as deep neural networks can be applied. A novel joint binary cross-entropy loss function is proposed to accomplish this goal. Its performance is compared to current state-of-the-art methods via simulation. The approach also shows improved accuracy in a probabilistic solar energy forecasting problem.
dc.description.sponsorshipThe authors’ work was partially supported by King Abdullah University of Science and Technology (grant number 3800.2).
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0167947321000372
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [, , (2021-02)] DOI: 10.1016/j.csda.2021.107203 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleDeep distribution regression
dc.typeArticle
dc.identifier.journalComputational Statistics & Data Analysis
dc.rights.embargodate2023-02-01
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
dc.contributor.institutionSchool of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia.
dc.identifier.pages107203
dc.identifier.arxivid1903.06023
kaust.grant.number3800.2
dc.date.published-online2021-02-22
dc.date.published-print2021-07


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