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Stochastic restricted estimation in partially linear additive errors-in-variables models

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Abstract

As a generalization of additive model and partially linear model, partially linear additive model has been paid considerably attention in recent years. This paper considers estimation of the parametric component of the semiparametric model when the covariates in the linear part are measured with additive error and some additional stochastic linear restrictions on the parametric component are available. Based on the corrected profile least-squares approach and mixed regression estimation method, we propose a corrected profile mixed estimator for the parametric component, and derive its asymptotic distribution. Finally, some simulation studies are conducted to illustrate the proposed procedure and the results are satisfactory.

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Acknowledgements

The authors would like to thank two anonymous referees and the Associate Editor for their constructed suggestions which significantly improved the presentation of the article. Chuanhua Wei’s research was supported by the National Natural Science Foundation of China (No. 11301565), Jin Yang’s research was supported by the National Natural Science Foundation of China (No. 11401502).

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Correspondence to Jin Yang.

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Wei, C., Yang, J. Stochastic restricted estimation in partially linear additive errors-in-variables models. Stat Papers 61, 1269–1279 (2020). https://doi.org/10.1007/s00362-018-0978-0

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  • DOI: https://doi.org/10.1007/s00362-018-0978-0

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