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Empirical likelihood for high-dimensional linear regression models

Author

Listed:
  • Hong Guo
  • Changliang Zou
  • Zhaojun Wang
  • Bin Chen

Abstract

High-dimensional data are becoming prevalent, and many new methodologies and accompanying theories for high-dimensional data analysis have emerged in response. Empirical likelihood, as a classical nonparametric method of statistical inference, has proved to possess many good features. In this paper, our focus is to investigate the asymptotic behavior of empirical likelihood for regression coefficients in high-dimensional linear models. We give regularity conditions under which the standard normal calibration of empirical likelihood is valid in high dimensions. Both random and fixed designs are considered. Simulation studies are conducted to check the finite sample performance. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Hong Guo & Changliang Zou & Zhaojun Wang & Bin Chen, 2014. "Empirical likelihood for high-dimensional linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(7), pages 921-945, October.
    • Handle: RePEc:spr:metrik:v:77:y:2014:i:7:p:921-945
    DOI: 10.1007/s00184-013-0479-z
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    References listed on IDEAS

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    Cited by:

    1. Lin, Fangzheng & Tang, Yanlin & Zhu, Zhongyi, 2020. "Weighted quantile regression in varying-coefficient model with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).

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