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Related Experiment Videos

Reference values obtained by kernel-based estimation of quantile regressions

G R Ducharme1, A Gannoun, M C Guertin

  • 1Département de Mathématiques et Statistique, Université de Montréal, Quebec, Canada.

Biometrics
|September 1, 1995
PubMed
Summary

This study introduces a new method for estimating quantile regression curves, crucial for calculating reference values. The proposed technique performs well, even when compared to existing parametric solutions.

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Area of Science:

  • Biostatistics
  • Medical Statistics
  • Nonparametric Statistics

Background:

  • Quantile regression is essential for establishing reference values in various medical and biological fields.
  • Accurate estimation of quantile regression curves is vital for understanding data dispersion and variability.

Purpose of the Study:

  • To develop and evaluate a non-parametric method for estimating quantile regression curves.
  • To assess methods for measuring the dispersion of quantile regression estimators.
  • To apply the proposed methods to establish reference values for fetal blood velocity.

Main Methods:

  • Utilized a symmetrized nearest-neighbor kernel estimator for conditional distributions.
  • Investigated two methods for measuring quantile regression estimator dispersion: asymptotic distribution and bootstrap method.

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  • Conducted a simulation study to assess the performance of the proposed methods.
  • Main Results:

    • The proposed automatic procedure for quantile regression estimation demonstrated robust performance.
    • Both asymptotic distribution and bootstrap methods proved effective in measuring estimator dispersion.
    • The methods showed favorable results even in scenarios where parametric models were available.

    Conclusions:

    • The developed non-parametric quantile regression approach is effective for computing reference values.
    • The proposed methods for dispersion measurement are reliable.
    • The approach is applicable to real-world data, such as fetal blood velocity reference values.