Statistical Hypotheses Testing Method for Fuzzy Multivariate Data;
本文给出了一个模糊多元数据的假设检验方法。
Multivariate data visualization is an important way of understand multivariate data.
多元数据可视化是理解多元数据的一种重要手段。
A new multivariate data visualization method named point-score parallel coordinates (PSPC) is proposed in this paper.
提出了一种多元数据的点得分平行坐标表示及可视化分析方法。
Non-negative matrix factorization (NMF) has been proposed for multivariate data analysis, with non-negativity constraints.
非 负数据处理的一种多元统计分析方法。
Pattern Recognition; Visualization; Multivariate Data; Graphical Representation; Geometric Algebra; Subspace Coordinates; Optimization.
模式识别；可视化；多元数据；图表示；几何代数；子空间坐标；优化。
You can carry out very comprehensive analysis of univariate and multivariate data based on linear, general linear, and mixed linear models.
你可以基于线性、广义线性、以及混合线性模型进行单变量和多变量数据的全面分析。
By far, the most important reason for performing a principal components analysis (PCA) is to use it as a tool for screening multivariate data.
到目前为止，最重要的原因为执行一个主成分分析(PCA)是使用它当作一个工具，用于筛选的多元数据。
Multivariate data sets suffer from the problem of representation, since a dimensionality above 3 is beyond the capability of plotting programs.
多元数据集遭受着表征的问题，因为三维以上超越了绘图程序的能力。
A subspace graphical representation model of multivariate data is proposed, which unites several traditional multivariate data visualization methods into the same representation framework.
本文提出多元数据的子空间坐标图表示模型，该模型可以将这些传统多元图表示方法统一到同一个表示框架。
However, these targets can not be performed easily especially with the complex multivariate data sets which can be obtained from most of the advanced chemical instruments and chemical plants.
然而，这些目标并不容易实现，特别是当我们处理从先进的化学仪器或化工厂获得的复杂多变量数据集时。
Just because your data is multivariate does not mean you only have to examine it with multivariate tools.
仅因为数据是多元的并不意味着就必须使用多元工具研究它。
CONCLUSION: multivariate random coefficients model can effectively analyze the dynamic change trend and random effects of multivariate repeated measures data in medical research.
结论：多变量随机系数模型可有效地进行多变量重复观测数据的动态变化趋势分析以及随机效应分析。
By using multivariate linear regression analysis, the mathematical model to classify and evaluate reservoir with well logging data was established.
通过多元线性回归分析，建立了用多种测井信息划分、评价储层的数学模型。
The horizontal prediction of thickness and porosity of a sand member in a region was made by applying multivariate statistical method to seismic attribute, drilling and logging data.
本文应用多元统计方法，利用地震属性并辅以钻井、测井资料对某地区某层段砂岩的厚度和孔隙率进行了横向预测。
The data were treated with univariate and multivariate analysis of variance, multiple linear regression analysis and t test.
数据处理方法包括单因素多变量方差分析、多因素线性回归分析和t检验。
Methods Univariate analysis and multivariate regression were performed on data of 117 patients with lupus nephritis, with the physicians global assessment of disease activity as the external criteria.
方法以临床医师的判断作为疾病活动程度的外部标准，对117例狼疮性肾炎患者的资料进行单因素相关分析和多元回归分析。
The hydrological method is using the hydrological series data to establish the autoregressive and multivariate recurrence models.
水文方法是利用水文序列资料建立自回归模型和多元递推模型。
Objective: To explore the results of different methods for managing multivariate missing data.
目的：探讨多变量缺失数据的不同处理方法对结果的影响。
Nonloss storage experiments were performed to a cryogenic vessel filled with liquid nitrogen, and a multivariate linearity regression analysis and check to the experimental data was also done.
对一低温容器利用液氮为介质进行了多次无损贮存实验，利用多元线性回归方法对实验数据进行分析和检验。
This paper deals with the multivariate vector analysis of quality data by geometric transformation, rotating coordinates and computation of the principal component of initial variables.
本文讨论多元质量数据的矢量分析方法，几何变换，坐标旋转和初始变量的主分量计算不同变量的主分量值。
We introduce and motivate the main theme of the course, the setting of the problem of learning from examples as the problem of approximating a multivariate function from sparse data - the examples.
我们介绍且激发课程的主题将朝向于实例学习法的问题设定，例如稀疏值中多变量函数近似的问题。
For modeling the multilevel few sample or irregular data, the accumulated multilevel statistical models of multivariate full model was built on ordinary multilevel statistical models.
为了解决多层的少样本或无规则数据的建模问题，在一般多层统计模型的基础上提出了多变量整体模式的累加多层统计模型。
AIM: to probe into the scientific evaluation method of multivariate physical examination data for the development of constitution and physical conditions of recruited male youth in Yunnan Province.
目的：探讨应征青年的体格发育和身体状况的多变量体检资料的科学评价方法。
Based on the analysing of the data, selected relevant factors, made a series of tests and amendments with models, then created forest volume estimation optimal multivariate linear regression model.
在分析数据的基础上，选择了相关遥感因子和定性因子，并通过一系列模型的检验与修正，建立了公顷蓄积量估测的最优多元线性回归模型。
The principal component analysis in multivariate statistic analysis is a method of compressing the dimension of vector data by extracting principal typical components from sample data set.
多元统计分析的主成份分析方法是对多维矢量数据提取主要特征分量，以此达到压缩矢量维数的目的。
Multivariate calibration models are of critical importance to many analytical measurements, particularly for near-infrared spectroscopic data.
多元校正模型在近红外光谱数据分析中具有非常重要的作用。
Principal Component Analysis (PCA) is a main multivariate statistical method for getting principal information from observational data.
主成分分析法是从观测数据中获取主要信息的一种多变量统计方法。
Conclusion: Multivariate and multilevel models can effectively analyze longitudinal data with several dependent variables.
结论：多元多层模型可有效地分析具有多个因变量的纵向研究资料。
However, there are a few on multivariate time series mining, since the data structure of multivariate time series is more complex than that of univariate time series.
然而多元时间序列的数据结构比一元时间序列更复杂，现有的理论和方法仍不够完善。
However, there are a few on multivariate time series mining, since the data structure of multivariate time series is more complex than that of univariate time series.
然而多元时间序列的数据结构比一元时间序列更复杂，现有的理论和方法仍不够完善。
应用推荐