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Parametric modal regression with error in covariates.

Qingyang Liu1, Xianzheng Huang1

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.

Biometrical Journal. Biometrische Zeitschrift
|January 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for modal regression analysis when covariates have measurement errors. It ensures accurate parameter estimation and includes a diagnostic tool for model validation, crucial for skewed data.

Keywords:
M-estimationbeta distributionbootstrapcorrected scoremodel misspecification

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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Modal regression is suitable for skewed and heavy-tailed response data.
  • Measurement error in covariates can lead to biased parameter estimation.
  • Existing methods may not adequately address measurement error in modal regression.

Purpose of the Study:

  • To propose an inference procedure for consistent parameter estimation in modal regression with measurement error.
  • To develop a diagnostic tool for assessing parametric assumptions using bootstrap methods.
  • To demonstrate the performance of the proposed methods with simulated and real-world data.

Main Methods:

  • Development of a novel inference procedure for modal regression.
  • Implementation of a score-based diagnostic tool utilizing parametric bootstrap.
  • Application to simulated datasets and real-world data for validation.

Main Results:

  • The proposed procedure provides consistent estimators for modal regression parameters.
  • The diagnostic tool effectively assesses the adequacy of model assumptions.
  • Empirical examples highlight the impact of accounting for measurement error.

Conclusions:

  • Accurate inference in modal regression requires addressing measurement error in covariates.
  • The developed methods offer a robust approach for analyzing complex datasets.
  • The study emphasizes the importance of diagnostic tools in statistical modeling.