对一般线性回归模型中有关参数估计分布的模拟问题，给出一种随机加权逼近的再构造方法。
A reconstructing method for random weighting approximations is proposed in approach to the distributions of the parameter estimates in general linear regression model.
本文在矩阵损失下研究了一般增长曲线模型中随机回归系数线性估计的可容许性。
We investigate the admissibility of the linear estimate of random regression coefficients under a matrix loss function in general growth curve models.
一般回归模型是通常线性模型的推广。
General regression model is a generalization of linear model.
提出了CUSI神经元模型的一般形式，给出其学习算法。通过实例将CUSI神经元模型应用到地质数据的分析上，取得了比线性回归更好的效果。
The general form of CUSI neuron model and its learning algorithm are given, and apply it to geological data analysis and get the better effect than linear regression.
利用线性回归建模时，一般都采用有常数项的模型，而不去考虑它对回归的作用是否显著。
There is a regression constant in the linear regression model. But it is not significant in some conditions.
利用线性回归建模时，一般都采用有常数项的模型，而不去考虑它对回归的作用是否显著。
There is a regression constant in the linear regression model. But it is not significant in some conditions.
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