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dc.contributor.advisor Scott, David W.
dc.creatorLane, Jonathan W.
dc.date.accessioned 2013-03-08T00:35:21Z
dc.date.available 2013-03-08T00:35:21Z
dc.date.issued 2012
dc.identifier.urihttps://hdl.handle.net/1911/70304
dc.description.abstract Quantile regression, a method used to estimate conditional quantiles of a set of data ( X, Y ), was popularized by Koenker and Bassett (1978). For a particular quantile q , the q th quantile estimate of Y given X = x can be found using an asymmetrically-weighted, absolute-loss criteria. This form of regression is considered to be robust, in that it is less affected by outliers in the data set than least-squares regression. However, like standard L 1 regression, this form of quantile regression can still be affected by multiple outliers. In this thesis, we propose a method for improving robustness in quantile regression through an application of Scott's L 2 Estimation (2001). Theoretic and asymptotic results are presented and used to estimate properties of our method. Along with simple linear regression, semiparametric extensions are examined. To verify our method and its extensions, simulated results are considered. Real data sets are also considered, including estimating the effect of various factors on the conditional quantiles of child birth weight, using semiparametric quantile regression to analyze the relationship between age and personal income, and assessing the value distributions of Major League Baseball players.
dc.format.extent 132 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectPure sciences
Quantile regression
Conditional quantiles
Last squares regression
Child birth weight
Statistics
dc.title Robust Quantile Regression Using L2E
dc.identifier.digital LaneJ
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.citation Lane, Jonathan W.. "Robust Quantile Regression Using L2E." (2012) Diss., Rice University. https://hdl.handle.net/1911/70304.


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