对参数特征值估计和可信区间的诠释都是得出样本数据推论的路径。
Point Estimates of parameters and Confidence Interval Interpretation are both means for making inferences about sample data.
本文提出了一种有限样本集上基于次特征值误差补偿和优势主向量上非对称分布的马氏距离改进算法。
A modification on Mahalanobis distance on samples of limited size by compensation for errors of non-dominant eigenvalues and asymmetrical distribution on dominant principle components is proposed.
找出了样本容量与测试精度要求及统计特征值的关系。
And the relationship between the sample content, and the measurement accuracy requirement and characteristic value is found out.
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