表明： 基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。
It is shown that the BP neural network method combined with the amino-acid composition and the biased auto-covariance function features could effectively improve the prediction accuracy.
建立不完全数据回归方程，给出回归系数的最佳无偏整体估计及其协方差矩阵。
The regression equation for incomplete data is established, and the best unbiased integral estimators of the regression parameters and their covariance matrix are also given.
对两个参数各提出了一个无偏估计并采用协方差改进法分别对其作了改进。
Unbiased estimation for the two parameters of the special model is proposed and improved by covariance adjustment approach, separately.
研究了任意秩多元线性模型中最优线性无偏预测的稳健性，即对任一线性可预测变量，得到了其关于协方差矩阵具有稳健性的充要条件。
The conditional optimal prediction of the conditional predictable variable in the multivariate linear model with arbitrary rank and linear equality constrains was investigated.
研究了任意秩多元线性模型中最优线性无偏预测的稳健性，即对任一线性可预测变量，得到了其关于协方差矩阵具有稳健性的充要条件。
The conditional optimal prediction of the conditional predictable variable in the multivariate linear model with arbitrary rank and linear equality constrains was investigated.
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