Nonparametric Regression with Correlated Errors

dc.contributor.author Opsomer, Jean
dc.contributor.author Wang, Yuedong
dc.contributor.author Yang, Yuhong
dc.contributor.department Statistics
dc.date 2018-02-16T21:33:37.000
dc.date.accessioned 2020-07-02T06:55:50Z
dc.date.available 2020-07-02T06:55:50Z
dc.date.issued 2000-05-01
dc.description.abstract <p>Nonparametric regression techniques are often sensitive to the presence of correlation in the errors. The practical consequences of this sensitivityare explained, including the breakdown of several popular data-driven smoothing parameter selection methods. We review the existing literature in kernel regression, smoothing splines and wavelet regression under correlation, both for short-range and long-range dependence. Extensions to random design, higher dimensional models and adaptive estimation are discussed.</p>
dc.description.comments <p>This preprint was published as Jean Opsomer, Yuedong Wang and Yuhong Yang, "Nonparametric Regression with Correlated Errors", <em>Statistical Science</em> (2001), 134-153, doi: <a href="http://dx.doi.org/10.1214/ss/1009213287" target="_blank">10.1214/ss/1009213287</a>.</p>
dc.identifier archive/lib.dr.iastate.edu/stat_las_preprints/108/
dc.identifier.articleid 1097
dc.identifier.contextkey 7443720
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_preprints/108
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90266
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_preprints/108/1999_OpsomerJ_NonparametricRegressionCorrelated.pdf|||Fri Jan 14 18:28:18 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords kernel regression
dc.subject.keywords splines
dc.subject.keywords wavelet regression
dc.subject.keywords adaptive estimation
dc.subject.keywords smoothing parameter selection
dc.title Nonparametric Regression with Correlated Errors
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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