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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Simulation results and a real dataset are also given to illustrate our the proposed methods.
📌 3 Key Takeaways
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Objective & Hypothesis: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.
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Key Findings & Data:Therefore, we extend composite quantile regression method to linear errors-in-variables models, and prove the asymptotic normality of the proposed estimators.
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Conclusion & Impact:Simulation results and a real dataset are also given to illustrate our the proposed methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Errors-in-variables · composite quantile regression
Abstract
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.
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Cite This Article
Jiang, R. (2015). Composite quantile regression for linear errors-in-variables models. Hacettepe Journal of Mathematics and Statistics, Volume: 44 Issue: 3. https://ajindex.com/articles/1403293