Saddlepoint approximations for the distribution of regression quantiles

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dc.contributor.authorDe Jongh P.J.
dc.contributor.authorDe Wet T.
dc.contributor.authorVan Deventer P.J.U.
dc.date.accessioned2011-05-15T16:02:31Z
dc.date.available2011-05-15T16:02:31Z
dc.date.issued2004
dc.description.abstractSince their introduction in 1978, regression quantiles have played an increasingly important role in regression analysis. In particular, they form the basis for quantile regression, a statistical technique intended to draw inferences about conditional quantile functions. Although it is well known that regression quantiles have limiting normal distributions, not much is known about their behaviour in finite and small samples. In the present paper we study this problem and, in particular, apply the saddlepoint method to obtain improved small sample approximations. These approximations are considered in a numerical study where they are found to perform quite well even in fairly small samples.
dc.description.versionArticle
dc.identifier.citationSouth African Statistical Journal
dc.identifier.citation38
dc.identifier.citation1
dc.identifier.issn0038271X
dc.identifier.urihttp://hdl.handle.net/10019.1/12511
dc.titleSaddlepoint approximations for the distribution of regression quantiles
dc.typeArticle
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