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Model estimation and selection for partial linear varying coefficient EV models with longitudinal data.

Mingtao Zhao1, Xiaoli Xu2, Yanling Zhu1

  • 1School of Statistics and Applied Mathematics Anhui University of Finance & Economics, Bengbu, People's Republic of China.

Journal of Applied Statistics
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a bias-corrected method for longitudinal partial linear varying coefficient errors-in-variables models with measurement errors. The approach simultaneously estimates and selects model components, offering improved accuracy for complex data.

Keywords:
Longitudinal datapartial linear varying coefficient EV modelsquadratic inference functionvariable selection

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Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Longitudinal data analysis presents challenges due to within-subject correlations.
  • Errors-in-variables (EV) models are crucial when covariates are measured with inaccuracies.
  • Partial linear varying coefficient models offer flexibility in capturing complex data structures.

Purpose of the Study:

  • To develop a robust statistical method for estimating and selecting components in longitudinal EV models.
  • To address challenges posed by measurement errors in covariates and within-subject correlations.
  • To simultaneously estimate parametric and nonparametric components within a unified framework.

Main Methods:

  • A bias-corrected penalized quadratic inference functions (QIF) method is proposed.
  • The method incorporates two penalty function terms for regularization and variable selection.
  • Asymptotic normality of parameter estimators and optimal convergence rates for nonparametric components are established under regularization conditions.

Main Results:

  • The proposed method effectively handles measurement errors in covariates and within-subject correlations.
  • Simultaneous estimation and selection of significant parametric and nonparametric components are achieved.
  • Simulation studies and a real-data analysis demonstrate the method's finite sample performance.

Conclusions:

  • The bias-corrected penalized QIF method provides a powerful tool for longitudinal EV models.
  • The approach offers accurate estimation and effective model selection for complex correlated data.
  • This methodology enhances the analysis of longitudinal data with covariate measurement error.