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Estimation of a functional single index model with dependent errors and unknown error density

Shang, Han Lin

Description

The problem of error density estimation for a functional single index model with dependent errors is studied. A Bayesian method is utilized to simultaneously estimate the bandwidths in the kernel-form error density and regression function, under an autoregressive error structure. For estimating both the regression function and error density, empirical studies show that the functional single index model gives improved estimation and prediction accuracies than any nonparametric functional...[Show more]

dc.contributor.authorShang, Han Lin
dc.date.accessioned2019-10-16T04:43:55Z
dc.identifier.issn0361-0918
dc.identifier.urihttp://hdl.handle.net/1885/177001
dc.description.abstractThe problem of error density estimation for a functional single index model with dependent errors is studied. A Bayesian method is utilized to simultaneously estimate the bandwidths in the kernel-form error density and regression function, under an autoregressive error structure. For estimating both the regression function and error density, empirical studies show that the functional single index model gives improved estimation and prediction accuracies than any nonparametric functional regression considered. Furthermore, estimation of error density facilitates the construction of prediction interval for the response variable.
dc.description.sponsorshipThis project was funded by a faculty research grant from the College of Business and Economics, Australian National University.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherTaylor & Francis Group
dc.rights© 2018 Taylor & Francis Group, LLC
dc.sourceCommunications in Statistics - Simulation and Computation
dc.titleEstimation of a functional single index model with dependent errors and unknown error density
dc.typeJournal article
local.description.notesImported from ARIES
dc.date.issued2018
local.identifier.absfor010401 - Applied Statistics
local.identifier.ariespublicationu1027566xPUB112
local.publisher.urlhttps://www.routledge.com/
local.type.statusPublished Version
local.contributor.affiliationShang, Hanlin, College of Business and Economics, ANU
local.description.embargo2037-12-31
local.identifier.doi10.1080/03610918.2018.1535068
dc.date.updated2019-05-05T09:03:47Z
CollectionsANU Research Publications

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