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dc.contributor.authorJung, Yoonsuh
dc.contributor.authorLee, Yoonkyung
dc.contributor.authorMacEachern, Steve N,
dc.contributor.editorLee, J
dc.date.accessioned2015-06-08T04:30:48Z
dc.date.available2014-11-01
dc.date.available2015-06-08T04:30:48Z
dc.date.issued2015
dc.identifier.citationJung, Y., Lee, Y., & MacEachern, S. N. (2015). Efficient quantile regression for heteroscedastic models. Journal of Statistical Computation and Simulation, 85(13), 2548–2568. http://doi.org/10.1080/00949655.2014.967244en
dc.identifier.issn0094-9655
dc.identifier.urihttps://hdl.handle.net/10289/9355
dc.description.abstractQuantile regression (QR) provides estimates of a range of conditional quantiles. This stands in contrast to traditional regression techniques, which focus on a single conditional mean function. Lee et al. [Regularization of case-specific parameters for robustness and efficiency. Statist Sci. 2012;27(3):350–372] proposed efficient QR by rounding the sharp corner of the loss. The main modification generally involves an asymmetric ℓ₂ adjustment of the loss function around zero. We extend the idea of ℓ₂ adjusted QR to linear heterogeneous models. The ℓ₂ adjustment is constructed to diminish as sample size grows. Conditions to retain consistency properties are also provided.
dc.format.mimetypeapplication/pdf
dc.rightsThis is an author’s accepted version of an article published in the journal: Journal of Statistical Computation and Simulation. © 2015 Taylor & Francis.
dc.subjectcheck loss function
dc.subjectheteroscedasticity
dc.subjectquantile regression
dc.titleEfficient quantile regression for heteroscedastic models
dc.typeJournal Article
dc.identifier.doi10.1080/00949655.2014.967244
dc.relation.isPartOfJournal of Statistical Computation and Simulation
pubs.begin-page2548
pubs.declined2014-11-21T09:50:20.217+1300
pubs.deleted2014-11-21T09:50:20.217+1300
pubs.elements-id117420
pubs.end-page2568
pubs.issue13
pubs.publication-statusAccepted
pubs.volume85


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