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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The simulation studies and a real data application are conducted to illustrate the finite sample performance of the proposed methods.
📌 3 Key Takeaways
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Objective & Hypothesis:It is known that composite quantile regression estimator could be much more efficient and sometimes arbitrarily more efficient than the least squares estimator.
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Key Findings & Data:The asymptotic behaviors of the proposed tests are established and their limiting null distributions are demonstrated to follow an asymptotically χ 2 -distribution.
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Conclusion & Impact:The simulation studies and a real data application are conducted to illustrate the finite sample performance of the proposed methods.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Hypotheses test · Composite quantile regression · Single-index model
Abstract
It is known that composite quantile regression estimator could be much more efficient and sometimes arbitrarily more efficient than the least squares estimator. In this paper, tests for the index parameter and index function in the single-index composite quantile regression are considered. The asymptotic behaviors of the proposed tests are established and their limiting null distributions are demonstrated to follow an asymptotically χ 2 -distribution. The simulation studies and a real data application are conducted to illustrate the finite sample performance of the proposed methods.
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Cite This Article
Jiang, R., Qian, W., Zhou, Z. (2014). Test for single-index composite quantile regression. Hacettepe Journal of Mathematics and Statistics, Volume: 43 Issue: 5. https://ajindex.com/articles/1403377