Variance estimation for semiparametric regression models by local averaging
ZHAO, Jingxin - Personal Name
PENG, Heng - Personal Name
HUANG, Tao - Personal Name
Variance estimation is a fundamental problem in statistical modelling and
plays an important role in the inferences after model selection and estimation. In this
paper, we focus on several nonparametric and semiparametric models and propose
a local averaging method for variance estimation based on the concept of partial
consistency. The proposed method has the advantages of avoiding the estimation of the
nonparametric function and reducing the computational cost and can be easily extended
to more complex settings. Asymptotic normality is established for the proposed local
averaging estimators. Numerical simulations and a real data analysis are presented to
illustrate the finite sample performance of the proposed method.
plays an important role in the inferences after model selection and estimation. In this
paper, we focus on several nonparametric and semiparametric models and propose
a local averaging method for variance estimation based on the concept of partial
consistency. The proposed method has the advantages of avoiding the estimation of the
nonparametric function and reducing the computational cost and can be easily extended
to more complex settings. Asymptotic normality is established for the proposed local
averaging estimators. Numerical simulations and a real data analysis are presented to
illustrate the finite sample performance of the proposed method.
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| EB00000004455K | Available |
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Statement of Responsibility
Jingxin Zhao 1, Heng Peng 1, Tao Huang 2
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