To determine the rank of regression coefficient matrix in a multivariate linear regression model, a model selection procedure is proposed based on the M-estimation.
为了确定多重线性回归模型中回归系数矩阵的秩，本文提出了一个基于M估计的模型选择程序，且在较弱的条件下建立了回归系数矩阵的秩的估计的强相合性。
Results show that iterative refinement using the SVD can improve regression coefficient estimates in the cases where the design matrix is highly collinear.
结果表明，在设计矩阵高度共线性时，用奇异值分解的迭代加细可以改进回归系数的估计。
A robust parameters estimation algorithm is proposed in this paper, which is based on uniform design for a linear regression model in the case of its coefficient matrix with random disturbance.
针对一个线性回归模型的系统矩阵存在的随机扰动情况，提出一种基于均匀设计的稳健参数估计算法。
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