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Estimation of partial linear error-in-response models with validation data

  • Estimation
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Abstract

In this paper, an estimation theory in partial linear model is developed when there is measurement error in the response and when validation data are available. A semiparametric method with the primary data is used to define two estimators for both the regression parameter and the nonparametric part using the least squares criterion with the help of validation data. The proposed estimators of the parameter are proved to be strongly consistent and asymptotically normaal, and the estimators of the nonparametric part are also proved to be strongly consistent and weakly consistent with an optimal convergent rate. Then, the two estimators of the parameter are compared based on their empirical performances.

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Authors and Affiliations

  1. Institute of Applied Mathematics, Academy of Mathematics and System Science, Chinese Academy of Science, No. A1, Nansijie, Zhongguancun, 100080, Beijing, China

    Qi-Hua Wang

  2. School of Science, Heilongjiang University, 150000, Harbin, China

    Qi-Hua Wang

Authors
  1. Qi-Hua Wang
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Additional information

Supported by NNSF of China (No. 10231030, No. 10241001) and a grant to the author for his excellent Ph.D. dissertation work in China.

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Wang, QH. Estimation of partial linear error-in-response models with validation data. Ann Inst Stat Math 55, 21–39 (2003). https://doi.org/10.1007/BF02530483

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  • DOI: https://doi.org/10.1007/BF02530483

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