Asymptotic Properties of Wavelet Estimators in Partially Linear Errors-in-variables Models with Long-memory Errors

While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables（EV） model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic...

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Bibliographic Details
Published in:Acta Mathematicae Applicatae Sinica 2018, Vol.34 (1), p.77-96
Main Authors: Hu, Hong-chang, Cui, Heng-jian, Li, Kai-can
Format: Article
Language:English
Subjects:
Applications of Mathematics
Asymptotic methods
Asymptotic properties
Computer simulation
Economic models
Estimating techniques
Estimators
Math Applications in Computer Science
Mathematical and Computational Physics
Mathematical models
Mathematics
Mathematics and Statistics
Random errors
Random variables
Regression analysis
Theoretical
Wavelet analysis
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Summary:While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables（EV） model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic distributions and weak convergence rates of the parametric and nonparametric estimators. At last, the validity of the wavelet method is illuminated by a simulation example and a real example.
ISSN:0168-9673
1618-3932
DOI:10.1007/s10255-018-0730-5