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<p>In this paper, we consider model estimation and variable selection for partial linear errors-in-variables models with longitudinal data through empirical likelihood and quadratic inference function methods. We propose a bias-corrected penalized empirical likelihood method that addresses both measurement errors in covariates and unknown within-subject correlations while performing model estimation and variable selection. Under some regularity conditions, the resulting estimators possess the oracle property, and the nonparametric function estimator achieves optimal convergence rates. Numerical results including simulation studies and real example analysis demonstrate that the proposed method makes sense in finite samples, validating its theoretical properties and practical applicability.</p>

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Bias-corrected penalized empirical likelihood for partial linear errors-in-variables models with longitudinal data

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Fanqun Li,
  • Jialong Kang,
  • Yan Fan,
  • Sanying Feng,
  • Mingtao Zhao

摘要

In this paper, we consider model estimation and variable selection for partial linear errors-in-variables models with longitudinal data through empirical likelihood and quadratic inference function methods. We propose a bias-corrected penalized empirical likelihood method that addresses both measurement errors in covariates and unknown within-subject correlations while performing model estimation and variable selection. Under some regularity conditions, the resulting estimators possess the oracle property, and the nonparametric function estimator achieves optimal convergence rates. Numerical results including simulation studies and real example analysis demonstrate that the proposed method makes sense in finite samples, validating its theoretical properties and practical applicability.