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    Robust Quantile Regression Using L2E

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    Author
    Lane, Jonathan W.
    Date
    2012
    Advisor
    Scott, David W.
    Degree
    Doctor of Philosophy
    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.
    Keyword
    Pure sciences; Quantile regression; Conditional quantiles; Last squares regression; Child birth weight; More... Statistics Less...
    Citation
    Lane, Jonathan W.. "Robust Quantile Regression Using L2E." (2012) Diss., Rice University. https://hdl.handle.net/1911/70304.
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    • Rice University Electronic Theses and Dissertations [13409]

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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map