Fully nonparametric regression for bounded data using dependent bernstein polynomials

dc.date.accessioned2018-11-29T15:36:43Z
dc.date.accessioned2022-10-18T21:27:13Z
dc.date.available2018-11-29T15:36:43Z
dc.date.available2022-10-18T21:27:13Z
dc.date.created2018-11-29T15:36:43Z
dc.date.issued2017
dc.identifierhttp://hdl.handle.net/10533/228472
dc.identifier1141057
dc.identifierWOS:000411424400028
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4459828
dc.description.abstractWe propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal extends the DirichletBernstein prior for single density estimation, by using dependent stick-breaking processes. A
dc.languageeng
dc.relationhttp://www.mat.uc.cl/~ajara/Publications_files/DependentBernstein.pdf
dc.relationhandle/10533/111556
dc.relation10.1080/01621459.2016.1180987
dc.relationhandle/10533/111541
dc.relationhandle/10533/108045
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.titleFully nonparametric regression for bounded data using dependent bernstein polynomials
dc.typeArticulo


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