Local Composite Quantile Regression

Event Details
  • Date/Time:
    • Thursday February 19, 2009 - Friday February 20, 2009
      10:00 am - 10:59 am
  • Location: Executive classroom 228 Main bldg
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Nicoleta Serban
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Summary Sentence: Local Composite Quantile Regression

Full Summary: Local Composite Quantile Regression

TITLE: Local Composite Quantile Regression

SPEAKER: Professor Runze Li


Local polynomial regression is a useful nonparametric regression tool to explore fine data structures ad has been widely used in practice. In this paper, we propose a new nonparametric regression technique called local composite quantile (CQR) smoothing in order to further improve the local polynomial regression. Sampling properties of the proposed estimation procedure are studied. We derive the asymptotic bias, variance and normality of the proposed estimate. Asymptotic relative efficiency of the proposed estimate with respect to the local polynomial regression is investigated. It is shown that the proposed estimate can be much more efficient than the local polynomial regression estimate for various non-normal errors, while being almost as efficient as the local polynomial regression estimate for normal errors. Simulation is conducted to examine the performance of the proposed estimates. The simulation results are consistent with our theoretic findings. A real data example is used to illustrate the proposed method.

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School of Industrial and Systems Engineering (ISYE)

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Polynomial regression
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Oct 12, 2009 - 4:36pm
  • Last Updated: Oct 7, 2016 - 9:47pm