School of Statistics and Management, Shanghai University of Finance and Economics
Recently, large scale datasets appear frequently due to the development of techniques. Distributed computation has attracted attentions from statistician. Since quantile regression has been an effective alternative to the classic mean regression in many fields. However, computationally efficient quantile regression estimates for large scale datasets are less developed. In this paper, we consider an efficient ADMM estimate that could be implemented in a distributed manner, which can deal with large scale datasets.
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