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dc.contributor.authorLipsitz, Michael
dc.contributor.authorBelloni, Alexandre
dc.contributor.authorChernozhukov, Victor V
dc.contributor.authorFernandez-Val, Ivan
dc.date.accessioned2019-11-07T17:36:24Z
dc.date.available2019-11-07T17:36:24Z
dc.date.issued2017-06
dc.identifier.urihttps://hdl.handle.net/1721.1/122791
dc.description.abstractThe R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.en_US
dc.language.isoen
dc.publisherEconomic & Social Research Councilen_US
dc.relation.isversionofhttp://dx.doi.org/10.1920/wp.cem.2017.2917en_US
dc.rightsCreative Commons Attribution 3.0 unported licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/en_US
dc.sourceThe R Journalen_US
dc.titlequantreg.nonpar: an R package for performing nonparametric series quantile regressionen_US
dc.title.alternativeQuantreg.nonpar: an R package for performing nonparametric series quantile regressionen_US
dc.typeArticleen_US
dc.identifier.citationLipsitz, Michael et al. "Quantreg.nonpar: an R package for performing nonparametric series quantile regression." cennmap (June 2017): CWP29/17en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Economics
dc.relation.journalcennmapen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-10-21T18:05:02Z
dspace.date.submission2019-10-21T18:05:06Z


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