可以设法克服疟疾历史数据不足的问题，也可设法建立模型和推定缺失数据。
There are ways around the lack of empirical data on malaria; there are ways to model and impute data that are missing.
本文讨论的是有缺失数据的双向有序方列联表的统计推断。
We discuss the statistics inference for bidirectionally ordinal square contingency tables with 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。
Objective: To explore the results of different methods for managing multivariate missing data.
这种方法既适合于全样本场合又适合于一般缺失数据场合。
This method is suitable not only to the complete sample case but also to the general missing data cases.
本文讨论的是有缺失数据的双向有序方列联表的统计推断。
We often meet missing data during analysis of contingency tables.
重点阐述其设计要点，以及如何利用多重插补方法对缺失数据进行处理。
The topics enclose the important phases of designing a split questionnaire, and the methods of using the multiple imputation method to deal with the missing data.
介绍了算法中如何处理高分枝属性、数值属性和缺失数据及剪枝等关键环节。
Some key aspects about algorithm are introduced here, such as how to deal with high-branching and numeric attributes, missing values as well as how to prune.
结果多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。
Results The multiple imputation method can impute missing values of the crossover design and generate valid statistical inferences.
结果：由缺失数据造成的信息缺失得到了弥补，综合评价结果的质量得到了提高。
Results: the information loss caused by missing data was recuperated and the quality of integrated assessment was improved.
有缺失数据两因素随机区组试验资料是两因素水平组合重复数不等的非平衡资料。
The data with missing values in two-factor experiment with randomized block design is the unbalanced data with unequal replications of level combination of two factors.
结果：多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。
RESULTS: The multiple imputation method imputed missing values of the crossover design and generated valid statistical inferences.
通过补全缺失数据、平滑噪声数据、消除不一致数据等技术，得到高质量的数据。
Filling missing values, smoothing noise data and removing inconsistent data were all adopted to get high quality data.
由于3维扫描点云通常存在噪音和缺失数据，提出了一种鲁棒的点云网格重建算法。
There is noise and defective data on the 3d scanning point cloud. A robust mesh reconstruction algorithm is proposed.
在不增加实验次数的情况下，缺失值估计是降低缺失数据对后续分析影响的有效方法。
Compared with increasing experiments, missing value estimating is preferred in reducing the influence of missing values on the post-processing.
在不增加实验次数的情况下，缺失值估计是降低缺失数据对后续分析影响的有效方法。
In microarray experiments, the missing value does exist and somewhat affects the stability and precision of the expression data analysis.
本文利用改进的K -均值算法对缺失数据进行处理，提高了朴素贝叶斯分类的精确度。
This paper USES the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier.
然而，众所周知，数据库中往往存在冗余数据、缺失数据、不确定数据和不一致数据等诸多情况，这些数据成了发现知识的一大障碍。
However, as well known, there are many issues in databases, such as redundant data, missing data, uncertain data, inconsistent data, and so on, they are the barriers to knowledge discovery.
将数据缺失方式划分为随机缺失，非随机缺失与完全随机缺失，这对于合理编写缺失数据推估程序，正确选用估算方法有很重要的意义。
It is very important to divide the missing data into three manners: missing at random (MAR), missing not at random (MNAR) and missing completely at random (MCAR).
是否有缺失的数据？
默认的或缺失的数据如何标准化？
斯洛伐克人，匈牙利人和缺失的数据。
这一层通常是预定义的，但是可以稍微对其进行客户化，如添加一些缺失的数据定义、关系和唯一命名标识等。
This layer is typically predefined, but can be slightly customized to add missing data definitions, relationships and unique name identifiers.
是否有缺失的或不完整的数据？
显然，这些记录中很大一部分包含错误的信息：错误、缺失和未及时发布的数据。
Apparently, a high percentage of those records contained incorrect information: data that was wrong, missing, or published before it should have been.
对于缺失或有冲突的数据，值可以在链接的记录之间传播，以便在所有链接的记录中提供名称或taxID的通用表示。
Values can be propagated across linked records for missing or conflicting data so that a common representation of name or tax ID will be present on all linked records.
这些定制可包括添加缺失的函数映射、修改昵称列的数据类型以及设置某些与性能相关的选项。
Such customization can include adding missing function mappings, altering the data type of a nickname column, and setting some performance-related options.
虽然联邦服务器可以弥补数据服务器上的功能缺失，但这要求操作发生在联邦服务器上。
The federated server can compensate for the lack of function at the data server, but doing so might require the operation to take place at the federated server.
Fiszbein说，过去要做这样的评价受到数据缺失和制作相反事实-即如果没有项目会是什么情况-的技术挑战的制约。
Fiszbein says in the past, such evaluations were constrained by the lack of data and the technical challenges of developing a counterfactual, that is, what would have happened without the program.
虽然这么做会花费数个小时，但是通过验证可以发现任何缺失的源数据或文档，这时修复这些问题花费的代价比在将来任何时候都要小。
This may take several hours but will identify any missing source data or documentation at a time when fixing the problem will be cheaper than at any time in the future.
也许dynamo——amazon将在SOSP 2007上演示的可伸缩数据存储——会是AWS拼图中缺失的那一块。
Perhaps Dynamo, Amazon's scalable data store and due to be presented at SOSP 2007, is the remaining missing piece of the AWS puzzle.
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