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Estimation for partially linear single-index spatial autoregressive model with covariate measurement errors

Author

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  • Ke Wang

    (Jilin University)

  • Dehui Wang

    (Liaoning University)

Abstract

This paper explores the estimators of parameters for a partially linear single-index spatial model which has measurement errors in all variables. We propose an efficient methodology to estimate our model by combining a local-linear smoother based Pseudo- $$\theta $$ θ algorithm, simulation-extrapolation (SIMEX) algorithm, the estimation equation and the estimation method for profile maximum likelihood. Under some regular conditions, we derive the asymptotic properties of the link function and unknown estimators. Some simulations indicate our estimation method performs well. Finally, we apply our method to a real data set of Boston Housing Price. The result shows that our model fits the data set well.

Suggested Citation

  • Ke Wang & Dehui Wang, 2024. "Estimation for partially linear single-index spatial autoregressive model with covariate measurement errors," Statistical Papers, Springer, vol. 65(7), pages 4201-4241, September.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:7:d:10.1007_s00362-024-01551-3
    DOI: 10.1007/s00362-024-01551-3
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    References listed on IDEAS

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    1. Lixing Zhu & Liugen Xue, 2006. "Empirical likelihood confidence regions in a partially linear single‐index model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 549-570, June.
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    4. Yiping Yang & Tiejun Tong & Gaorong Li, 2019. "SIMEX estimation for single-index model with covariate measurement error," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 137-161, March.
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    6. Suli Cheng & Jianbao Chen, 2021. "Estimation of partially linear single-index spatial autoregressive model," Statistical Papers, Springer, vol. 62(1), pages 495-531, February.
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