研究岭型主成分估计在岭型降维估计类中的性质。
The property of combining ridge and principal components estimate is studied in the ridged class of reduced-dimension estimators.
证明了在一类岭型降维估计中，岭型主成分估计的方差和最小。
It is proved that the combining ridge and principal components regression have the minimum variance sum in the class of reduced-dimension estimators.
三种方法分别是稳健主成分估计、稳健岭估计和稳健根方估计。
Finally, we obtained three robust biased model fitting methods which were called as robust principal component estimate, robust ridge estimate and robust root-root estimate respectively.
干涉仪雷达系统性误差的模拟计算表明：效果良好，优于主成分估计。
The simulation calculation of radar systematic error shows that the results are satisfactory and better than those of principal component estimate.
本文研究了在均方误差意义下岭型主成分估计在岭型降维估计类中的最优性。
The optimality of the MSE of combining ridge and principal components in the class of combining ridge and reduced dimension estimators is dealt with in this paper.
讨论岭型主成分估计在一类降维估计中的方差性质，证明了它的方差和在这类降维估计中最小。
This paper deals with the variance property of combining ridge and principal components estimate in the class of reduced-dimension estimates.
结果表明：抗差主成分估计不仅可以解除法方程系数降的病态，而且对于抑制观测粗差的影响也有显著功效。
The results show that the robust principal component estimation can not only overcome the ill condition of the normal equation. but also resist the outlier effects to the model parameters.
本文基于最小二乘方法和主成分估计思想，构造了一种丢失数据的稳健修复算法，并可对失真数据予以修正。
Based on the least square method and main elements analysis, this paper proposed a robust algorithm to repair the lost and distortion data.
研究岭型主成分估计在降维估计类中的方差最优性，证明了它的方差阵在降维估计类中最小，方差阵的特征值最小，方差和及方差积最小。
This paper considers the classification compression principal component estimate of regression coefficient in growth curve model and proves that it is superior to least squares estimate.
利用辅助天线输出中的干扰成分，自适应的调整其加权系数，最大程度的估计出主天线输出中的干扰。
Using an adaptive process, the weights of the auxiliary antenna outputs are adjusted, and the interference in the main antenna output are estimated.
基于多层核主成分提取估计器需要将调制信号的训练样本根据各自的频率进行分层。
The estimator based on kernel principal component extraction requires to stratify the training samples of interested signals with respect to their respective frequencies.
主成分分析能有效估计这一几何体的本征维数。
The principal component analysis can estimate the intrinsic dimensionality of the hyper-plane.
主成分分析能有效估计这一几何体的本征维数。
The principal component analysis can estimate the intrinsic dimensionality of the hyper-plane.
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