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Related Experiment Video

Updated: May 26, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints.

Yufeng Liu1, Yichao Wu

  • 1Department of Statistics and OR, Carolina Center for Genome Sciences, University of North Carolina, 354 Hanes Hall, CB 3260, Chapel Hill, NC 27599, USA.

Journal of Nonparametric Statistics
|December 23, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces simultaneous non-crossing quantile regression (SNQR), a novel kernel-based method for estimating multiple conditional quantiles. SNQR improves estimation accuracy and ensures quantile functions do not cross, outperforming individual quantile regression methods.

Related Experiment Videos

Last Updated: May 26, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Statistics
  • Econometrics

Background:

  • Quantile regression (QR) is vital for modeling the relationship between a response variable and covariates.
  • Estimating multiple conditional quantile functions simultaneously offers advantages over individual estimation, including enhanced accuracy and the ability to enforce non-crossing constraints.

Purpose of the Study:

  • To propose a novel kernel-based multiple quantile regression technique, simultaneous non-crossing quantile regression (SNQR).
  • To develop both unregularised and regularised versions of SNQR.
  • To establish asymptotic properties for linear SNQR and oracle properties for sparse linear SNQR.

Main Methods:

  • Kernel-based estimation for multiple quantile functions.
  • Incorporation of non-crossing constraints on kernel coefficients.
  • Development of theoretical properties including asymptotic normality and oracle properties.

Main Results:

  • The proposed simultaneous non-crossing quantile regression (SNQR) method effectively estimates multiple conditional quantiles.
  • Both unregularised and regularised SNQR techniques were developed and analyzed.
  • Theoretical properties, such as asymptotic normality and oracle properties, were established for linear and sparse linear SNQR.

Conclusions:

  • Simultaneous non-crossing quantile regression (SNQR) provides a statistically robust and accurate method for estimating multiple conditional quantiles.
  • SNQR offers improved estimation accuracy compared to individual quantile regression methods.
  • The developed theoretical properties support the application of SNQR in statistical modeling.