| Distributed Penalized Modal Regression for Massive Data |
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| 作者姓名： | JIN Jun LIU Shuangzhe MA Tiefeng |
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| 作者单位： | 1. College of Mathematical Sciences, Yangzhou University;2. Faculty of Science and Technology, University of Canberra;3. School of Statistics, Southwestern University of Finance and Economics |
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| 基金项目： | the National Natural Science Foundation of China under Grant No. 11471264； |
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| 摘 要： | Nowadays, researchers are frequently confronted with challenges from massive data computing by a number of limitations of computer primary memory. Modal regression(MR) is a good alternative of the mean regression and likelihood based methods, because of its robustness and high efficiency. To this end, the authors extend MR to massive data analysis and propose a computationally and statistically efficient divide and conquer MR method(DC-MR). The major novelty of this method consists of splitting ...
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