Aim: To summarize the trend test analysis methods for two-way ordinal categorical data.
目的：探讨双向有序分类数据相关的趋势检验方法。
Basic to any analysis of categorical data is a consideration of how the data was collected.
任何范畴数据分析的基础在于考虑数据是怎样收集的。
The statistical methods for categorical data have been discussed with examples in clinical papers.
以几篇临床论文实例讨论分类断据的统计方法。
They may also be asked to conduct categorical data analysis, robust estimation or survival analysis.
他们也可能会要求进行分类数据分析，稳健估计或生存分析。
Objective To explore the ROC analysis methods for repeated ordinal categorical data in diagnostic test.
目的探讨诊断试验中重复有序分类测量数据的ROC分析方法。
Objective The statistical method for repeated measures categorical data analysis was introduced and applied to clinical trials.
目的介绍分类数据重复测量资料的统计分析及其在临床试验中的应用。
His interests include statistical models for categorical data, recommendation systems, and optimal choice of Web site display components.
他的兴趣包括分类数据的统计模型、推荐系统，以及网站展示内容的最佳选择。
This paper introduces a general methodology for the analysis of repeated measurement of categorical data. A clinical example is illustrated.
本文介绍分析重复测量分类数据的一般统计方法，并用临床资料进行实例分析。
For categorical data the relative risk (RR), risk difference (RD) and number needed to treat (NNT) with 95% confidence intervals (CI) were calculated.
对分类数据计算相对危险度(RR)，风险差(RD)和需治疗人数(NNT)及其95%可信区间(CI)。
Aim CDT (categorical data type) is a parallel model basing on category theory, and this paper discusses the CDT construction of the memory type in details.
目的CDT(范畴数据类型)是以范畴理论为基础的并行计算模型，本文对存储器类型的CDT构造进行深入的探讨。
The influencing factors of feat technique in hygiene were investigated in Sichuan with the method of the stratified sampling and the categorical data analysis.
采用分层抽样方法和分类资料统计方法，开展四川省农村卫生适宜技术推广影响因素调查研究。
Owing to the sparsity of high-dimensional data and the features of categorical data, it needs to develop special methods for high-dimensional categorical data.
现有的数据聚类方法仍存在着各种不足，聚类速度和结果的质量不能满足大型、高维数据库上的聚类需求。
Objective To explore the application of nonlinear mixed models in ordered categorical data of cross-over trial analysis and hence provide methodology reference.
目的探讨非线性混合效应模型在交叉试验等级资料中的应用，为临床试验交叉设计资料的正确分析提供方法学参考。
So the relations of homomorphism are key ideas in construction of categorical data types, however there are some functions that are not homomorphism in data types.
因此同态的关系是构造范畴数据类型关键，但是，在数据类型中有些操作并不是同态操作。
Objective:To explore the application of generalized estimating equation in ordered categorical data of cross-over trial analysis and provide methodology reference.
目的：探讨广义估计方程在交叉试验等级资料中的应用，为临床试验交叉设计资料的正确分析提供方法学参考。
The statistical methods for categorical data have been discussed with examples in clinical papers. we ought to pay attention to selecting rank sum test for data of ordinal categories.
以几篇临床论文实例讨论分类断据的统计方法。对有序分类数据注意选择和检验。
This paper propose a refined self-organizing map that can directly handle categorical data or hybrid data, map the data to lower dimensions, and also uncover the similarity among data.
本研究针对此问题，提出改良式自组映射图，能直接处理种类型态或混合型态的资料，同时在投射后的低维度空间，反映高维度资料之间的相似度。
However, conventional SOMs handle only numerical data, categorical data has to be converted to Boolean data resulting in unable to disclosure the similarity among the high-dimensional data.
然而，传统自组映射图只能处理数值型资料，种类型资料必须透过编码转换成一群二元数值型态资料，因而无法反映种类型资料值之间的相似程度。
Data were pooled across studies, and relative risks for categorical outcomes and weighted mean differences for continuous outcomes, weighted according to study sample size, were calculated.
将所有的研究的数据合成，对于分类变量效应指标为风险比，对于连续型变量效应指标为加权均数差，根据研究样本的大小来加权。
At present, the clustering analysis about numeric valued data is quite mature, but the clustering analysis about categorical valued and mixed valued data is not perfect.
目前对于数值属性数据的聚类分析已经相当成熟，而对类属性和混合属性数据的聚类分析则并不十分完善。
Composed of those categorical variables, the enumeration data had to be correspondingly analyzed with special statistical methods on the ground of their design methods.
由这些变量组成的计数资料必须根据资料设计的特点，进行相应的统计学分析。
In clinical research, the collected data cover quite of categorical variables.
在临床科研中，收集的数据中往往包含了一些分类变量。
In clinical research, the collected data cover quite of categorical variables.
在临床科研中，收集的数据中往往包含了一些分类变量。
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