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 计算数学 2018, Vol. 40 Issue (4): 402-417    DOI:
 论文 最新目录 | 下期目录 | 过刊浏览 | 高级检索 Previous Articles  |  Next Articles THE STUDY OF ROBUST MATRIX REGRESSION MODELS AND ALGORITHMS
Chen Bingzhen, Kong Lingchen, Shang Pan
School of Science, Beijing Jiaotong University, Beijing 100044, China
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Abstract： With the breakout of the big data era, the data we are interested in is more and more complex. Especially, the model with matrix coefficient is in urgent to be constructed and solved. Many scholars are devoted to studying the statistical property analysis and designing algorithms for solving the matrix model. When the random errors have expectation 0 and the same variance, the method based on the least square loss function may perform well. However, when the random errors are heteroscedastic or the distribution of the errors are heavy-tailed (such as bi-exponential distribution, t-distribution, etc.) or the data contain outliers, the robust methods should be considered. The common robust methods are LAD, Quantile, Huber, etc. Most of the current research on the robust methods focus on the linear regression problem. There is few research on matrix regression problem. In this paper, we start with the least squares models, then summarize and comment on some matrix regression models. At the same time, we list some papers and briefly introduce some of our recent work. Finally, for the robust matrix regression problem, we propose some ideas and prospects.

 引用本文: . 稳健矩阵回归模型和方法研究[J]. 计算数学, 2018, 40(4): 402-417. . THE STUDY OF ROBUST MATRIX REGRESSION MODELS AND ALGORITHMS[J]. Mathematica Numerica Sinica, 2018, 40(4): 402-417.

  Amin M, Song L, Thorlie M A, Wang X. SCAD-Penalized Quantile Regression For HighDimensional Data Analysis And Variable Selection[J]. Statistica Neerlandica, 2015, 69(3):212-235. Arslan O. Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression[J]. Computational Statistics and Data Analysis, 2012, 56(6):1952-1965. 常象宇, 徐宗本, 张海, 王建军, 梁勇. 稳健lq(0
  赵志宁, 石全, 张军刚. 基于改进离散粒子群优化算法的作战弹药分配研究[J]. 计算数学, 2013, 34(3): 205-211.  黄力明. 基于混沌微粒群优化算法的阈值图像分割[J]. 计算数学, 2008, 29(2): 119-125.
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