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Focused Information Criterion and Model Averaging with Generalized Rank Regression
Qingzhao Zhang1,2, Xiaogang Duan3, Shuangge Ma1,2
1School of Economics and the Wang Yanan Institute for Studies in Economics, Xiamen University.
Statistics & Probability Letters
|June 2, 2017
Summary
Generalized rank regression offers robust estimation by using weighted ranks. This study establishes its asymptotic properties under misspecification and applies it to focus information criterion and frequentist model averaging.
Area of Science:
- Statistics
- Econometrics
Background:
- Weighted rank regression provides robust estimation methods.
- Generalized rank regression utilizes factor space for weight determination.
- Robust estimation is crucial for reliable statistical inference.
Purpose of the Study:
- To establish the asymptotic properties of generalized rank regression under local model misspecification.
- To apply generalized rank regression to focus information criterion and frequentist model averaging.
- To determine the properties of these combined methods.
Main Methods:
- Asymptotic analysis of generalized rank regression.
- Application of generalized rank regression within focus information criterion framework.
- Integration of generalized rank regression with frequentist model averaging.
Main Results:
- The asymptotic properties of generalized rank regression are established under local model misspecification.
- The application of generalized rank regression to focus information criterion and frequentist model averaging is demonstrated.
- The properties of these advanced statistical techniques are successfully derived.
Conclusions:
- Generalized rank regression is a theoretically sound method for robust estimation.
- The integration of generalized rank regression with information criteria and model averaging enhances statistical modeling.
- This research provides a foundation for robust statistical inference in complex models.
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