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<p>This paper studies a partially linear varying coefficient spatial autoregressive model with covariate measurement errors. When some covariates are measured with additive errors, statistical modeling that ignores these measurement errors may lead to biased results. To estimate the unknown parameters and coefficient functions, a measurement errors-corrected profile quasi-maximum likelihood approach based on the local-linear method is introduced. Under some regular conditions, we derive the consistency and asymptotic normality of the estimators for parameters and coefficient functions. Some simulation studies are conducted to illustrate the performance of the proposed estimators and the results are satisfactory. Finally, a real dataset example is given to demonstrate the application of the proposed method.</p>

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Corrected profile likelihood estimation for semiparametric varying-coefficient spatial autoregressive models with measurement errors

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Xinyan Li,
  • Ke Wang,
  • Dehui Wang

摘要

This paper studies a partially linear varying coefficient spatial autoregressive model with covariate measurement errors. When some covariates are measured with additive errors, statistical modeling that ignores these measurement errors may lead to biased results. To estimate the unknown parameters and coefficient functions, a measurement errors-corrected profile quasi-maximum likelihood approach based on the local-linear method is introduced. Under some regular conditions, we derive the consistency and asymptotic normality of the estimators for parameters and coefficient functions. Some simulation studies are conducted to illustrate the performance of the proposed estimators and the results are satisfactory. Finally, a real dataset example is given to demonstrate the application of the proposed method.