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Efficient statistical inference for partially nonlinear errors-in-variables models

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Abstract

In this paper, we consider the partially nonlinear errors-in-variables models when the nonparametric component is measured with additive error. The profile nonlinear least squares estimator of unknown parameter and the estimator of nonparametric component are constructed, and their asymptotic properties are derived under general assumptions. Finite sample performances of the proposed statistical inference procedures are illustrated by Monte Carlo simulation studies.

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Authors and Affiliations

  1. College of Applied Sciences, Beijing University of Technology, Beijing, 100124, P. R. China

    San Ying Feng & Gao Rong Li

  2. College of Mechanical Engineering, Beijing Information Science and Technology University, Beijing, 100192, P. R. China

    Jun Hua Zhang

Authors
  1. San Ying Feng
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  2. Gao Rong Li
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  3. Jun Hua Zhang
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Corresponding author

Correspondence to Gao Rong Li.

Additional information

Supported by National Natural Science Foundation of China (Grant Nos. 11101014 and 11002005), the Beijing Natural Science Foundation (Grant No. 1142002), the Doctoral Fund of Innovation of Beijing University of Technology, the Science and Technology Project of Beijing Municipal Education Commission (Grant No. KM201410005010) and the Training Programme Foundation for the Beijing Municipal Excellent Talents (Grant No. 2013D005007000005)

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Feng, S.Y., Li, G.R. & Zhang, J.H. Efficient statistical inference for partially nonlinear errors-in-variables models. Acta. Math. Sin.-English Ser. 30, 1606–1620 (2014). https://doi.org/10.1007/s10114-014-1358-x

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  • DOI: https://doi.org/10.1007/s10114-014-1358-x

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