Composite quantile regression for linear errors-in-variables models
Composite quantile regression can be more efficient and sometimes arbitrarily more efficient than least squares for non-normal random errors,
and almost as efficient for normal random errors. Therefore, we extend composite quantile regression method to linear errors-in-variables
models, and prove the asymptotic normality of the proposed estimators. Simulation results and a real dataset are also given to illustrate
our the proposed methods.
Keywords:
Errors-in-variables composite quantile regression,
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- Yayın Aralığı: 6
- Başlangıç: 2002
- Yayıncı: Hacettepe Üniversitesi Fen Fakultesi
Sayıdaki Diğer Makaleler
Maozu Lu, Gang Yu, Weiguo Wang, Shaoping Wang