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On studentized residuals in the quantile regression framework

Show simple item record Ranganai, Edmore 2017-02-10T17:32:16Z 2017-02-10T17:32:16Z 2016-08-02
dc.identifier.citation SpringerPlus. 2016 Aug 02;5(1):1231
dc.description.abstract Abstract Although regression quantiles (RQs) are increasingly becoming popular, they are still playing a second fiddle role to the ordinary least squares estimator like their robust counterparts due to the perceived complexity of the robust statistical methodology. In order to make them attractive to statistical practitioners, an endeavor to studentize robust estimators has been undertaken by some researchers. This paper suggests two versions of RQs studentized residual statistics, namely, internally and externally studentized versions based on the elemental set method. The more preferred externally studentized version is compared to the one based on standardized median absolute deviation (MAD) of residuals using a well-known data set in the literature. While the MAD based outlier diagnostic seemed to be uniform and more aggressive to flagging outliers the RQ externally studentized one exhibited a dynamic pattern consistent with RQ results.
dc.title On studentized residuals in the quantile regression framework
dc.type Journal Article 2017-02-10T17:32:16Z
dc.language.rfc3066 en
dc.rights.holder The Author(s)

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