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Focused information criterion and model averaging with generalized rank regression

Author

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  • Zhang, Qingzhao
  • Duan, Xiaogang
  • Ma, Shuangge

Abstract

Generalized rank regression, which is a class of weighted rank regression with weights based on factor space, provides a powerful tool for conducting robust estimation. In this article, we first establish the asymptotic properties of generalized rank regression under local model misspecification. We then apply the generalized rank regression to the focus information criterion and frequentist model averaging and establish their properties.

Suggested Citation

  • Zhang, Qingzhao & Duan, Xiaogang & Ma, Shuangge, 2017. "Focused information criterion and model averaging with generalized rank regression," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 11-19.
    • Handle: RePEc:eee:stapro:v:122:y:2017:i:c:p:11-19
    DOI: 10.1016/j.spl.2016.10.020
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    References listed on IDEAS

    as
    1. Terpstra, Jeff T. & McKean, Joseph W., 2005. "Rank-Based Analysis of Linear Models Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i07).
    2. repec:cup:cbooks:9780521852258 is not listed on IDEAS
    3. Lan Wang & Runze Li, 2009. "Weighted Wilcoxon-Type Smoothly Clipped Absolute Deviation Method," Biometrics, The International Biometric Society, vol. 65(2), pages 564-571, June.
    4. Gerda Claeskens & Raymond J. Carroll, 2007. "An asymptotic theory for model selection inference in general semiparametric problems," Biometrika, Biometrika Trust, vol. 94(2), pages 249-265.
    5. Ganggang Xu & Suojin Wang & Jianhua Z. Huang, 2014. "Focused information criterion and model averaging based on weighted composite quantile regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 365-381, June.
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    Cited by:

    1. Guozhi Hu & Weihu Cheng & Jie Zeng, 2023. "Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data," Mathematics, MDPI, vol. 11(3), pages 1-21, February.

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