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DGQR estimation for interval censored quantile regression with varying-coefficient models.

ChunJing Li1, Yun Li1, Xue Ding1

  • 1School of Mathematics and Statistics, Changchun University of Technology, Changchun, China.

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|November 10, 2020
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Summary

This study introduces a direct generalization quantile regression estimation method for interval-censored data. The new approach offers consistent and asymptotically normal estimators, even when censoring is not uniform.

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Quantile regression is crucial for understanding data distributions beyond the mean.
  • Varying-coefficient models offer flexibility in capturing complex relationships.
  • Interval-censored data presents unique challenges in statistical modeling.

Purpose of the Study:

  • To propose a direct generalization quantile regression estimation method (DGQR) for varying-coefficient models with interval-censored data.
  • To extend existing methods for complete observed data to handle interval-censored scenarios.
  • To develop an estimation technique that does not require identically distributed censoring vectors.

Main Methods:

  • Developed a direct generalization quantile regression estimation method (DGQR).
  • Employed techniques to ensure consistency and asymptotic normality of the proposed estimators.
  • Validated the method using simulation studies and a real-world data analysis.

Main Results:

  • Established the consistency and asymptotic normality properties of the DGQR estimators.
  • Demonstrated the method's effectiveness through simulation studies.
  • Successfully applied the DGQR method to a real data example, showcasing its practical utility.

Conclusions:

  • The proposed DGQR estimation method is a robust and effective approach for quantile regression with interval-censored data.
  • The method's ability to handle non-identically distributed censoring vectors enhances its applicability.
  • The DGQR method provides a valuable tool for analyzing complex data structures in various scientific fields.