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Nonlinear parametric quantile models.

Matteo Bottai1, Giovanna Cilluffo2

  • 1Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.

Statistical Methods in Medical Research
|July 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces nonlinear parametric quantile models to improve estimation accuracy. These models offer a flexible approach for analyzing complex data, balancing bias and variability in quantile regression.

Keywords:
Forced oscillation techniqueintegrated loss functionparametricquantile regressionquantile regression coefficients models

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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Quantile regression estimates conditional quantiles but can yield erratic results.
  • Existing parametric models are limited to linear forms in parameters and covariates.

Purpose of the Study:

  • To present and analyze nonlinear parametric quantile models.
  • To extend quantile regression flexibility beyond linear constraints.

Main Methods:

  • Development of a general nonlinear parametric quantile model framework.
  • Description of estimator properties and asymptotic behavior.
  • Assessment via simulation studies and real-world data applications.

Main Results:

  • The proposed nonlinear models offer improved estimation over traditional methods.
  • Demonstrated ability to handle complex relationships (nonlinear in parameters/covariates).
  • Successful application in epidemiological and toxicological studies.

Conclusions:

  • Nonlinear parametric quantile models provide a robust alternative for estimating conditional quantiles.
  • These models enhance the analysis of complex data, including extreme quantiles and dose-response relationships.