A reconstructing method for random weighting approximations is proposed in approach to the distributions of the parameter estimates in general linear regression model.
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。
Zero-truncated count model could not only solve the issue of zero-truncated count distribution, but also the parameter estimates were more accurate, the fitting results were more reasonable.
采用零截尾计数模型分析，不仅可以解决零截尾计数分布问题，且参数估计结果更准确，拟合效果更合理。
Algorithms for iteratively refining the parameter estimates and residuals from the fitting of a regression model using QR decomposition are described.
讨论用QR分解拟合回归方程时，参数估计和剩余的迭代加细算法。
Finally, conclusions on failure correlations have been made upon the model and parameter estimates.
最后，论文基于推导出的模型参数，得出与部件故障相关性有关的若干结论。
Results to simulate data according to the case model, and parameter estimates are nearly consistent with the primary model.
结果按照实例模型进行模拟数据，参数估计与原模型几乎一致。
Abstract: Under the matrix loss function, the necessary and sufficient conditions of linear admissible estimates of nonestimatible parameter functions for a general linear model are obtained.
文摘：一般线性模型可估函数的可容许估计问题已有详细的讨论。对一般线性模型在矩阵损失下，得到了不可估函数的线性估计为可容许估计的充要条件。
Abstract: Under the matrix loss function, the necessary and sufficient conditions of linear admissible estimates of nonestimatible parameter functions for a general linear model are obtained.
文摘：一般线性模型可估函数的可容许估计问题已有详细的讨论。对一般线性模型在矩阵损失下，得到了不可估函数的线性估计为可容许估计的充要条件。
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