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Yapay Zeka Hızlı Bakış & Çıkarımlar
AI Insight⚡ TL;DR(1 Cümlede Hızlı Çıkarım)
Simulation results and a real dataset are also given to illustrate our the proposed methods.
📌 3 Maddede Bu Makale
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Amaç & Hipotez: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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Temel Bulgular & Veri: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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Sonuç & Katkı:Simulation results and a real dataset are also given to illustrate our the proposed methods.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler: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.
Anahtar kelimeler
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Bu Makaleye Atıf Yap
Jiang, R. (2022). Composite quantile regression for linear errors-in-variables models. Hacettepe Journal of Mathematics and Statistics, Cilt: 44 - Sayı: 3, 707–713. https://ajindex.com/articles/530217