Adaptive Huber Regression on Markov-dependent Data

Jianqing Fan Yongyi Guo Bai Jiang

Statistics Theory and Methods mathscidoc:1912.43437

arXiv preprint arXiv:1904.09027, 2019.4
High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For convenience of theoretical analyses, classical methods usually assume independent observations and subGaussian-tailed errors. However, neither of them hold in many real high-dimensional time-series data. Recently [Sun, Zhou, Fan, 2019, J. Amer. Stat. Assoc., in press] proposed Adaptive Huber Regression (AHR) to address the issue of heavy-tailed errors. They discover that the robustification parameter of the Huber loss should adapt to the sample size, the dimensionality and (1+\delta) -moments of the heavy-tailed errors. We progress in a vertical direction and justify AHR on dependent observations. Specially, we consider an important dependence structure---Markov dependence. Our results show that the Markov dependence impacts on the adaption of the robustification parameter and the estimation of regression coefficients in the way that the sample size should be discounted by a factor depending on the spectral gap of the underlying Markov chain.
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  title={Adaptive Huber Regression on Markov-dependent Data},
  author={Jianqing Fan, Yongyi Guo, and Bai Jiang},
  booktitle={arXiv preprint arXiv:1904.09027},
Jianqing Fan, Yongyi Guo, and Bai Jiang. Adaptive Huber Regression on Markov-dependent Data. 2019. In arXiv preprint arXiv:1904.09027.
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