解决该问题的最好办法是对缺失数据进行准确估计。
To solve the problem, the best way is to estimate the missing data as accurately as possible.
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.
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.
你可能会经历与该表相关的信息缺失甚至在某些情况下会缺失数据。
You may experience the information related to this watch to be short of break can be short of below certain circumstance even lose data.
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.
提出了一种基于马氏距离的填充算法来估计基因表达数据集中的缺失数据。
A imputation method based on Mahalanobis distance was proposed to estimate missing values in the gene expression data.
结论多重填补与多重填补分析为处理存在缺失数据的资料提供了有效的策略。
Conclusion mi and MIANALYZE procedures provide a valid strategy for handling data set with missing values.
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.
医院季节性时间序列分析中，会出现缺失数据和异常值，这就影响了预测预报。
When we analyse seasonal series of hospital data, there are missing values and outliers. It is difficulty for forecasting.
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.
原始点云经梯度方向迭代移动后，过滤噪音和剔除离群点，并修补点云缺失数据。
Points are moved onto the iso-surface by an iterative clustering along gradient field, where the noise and outliers are removed and defective data are repaired.
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.
利用径向基人工神经网络（RBF）同时具有自组织神经网络和回归网络的优点，可以对缺失数据进行预测。
The RBF network possesses the advantages of Kohonen and regression networks. A test was performed to prove the effectiveness of RBF to complement the incomplete spatial information.
方法用MI对缺失数据进行填补，用标准的统计程序对填补后的数据集分析，最后用多重填补分析综合各个数据集的统计分析结果。
Methods Using MI to fill in missing data and analyzing the multiply imputed data sets with standard statistical procedure, then combining the statistical inferences with MIANALYZE procedure.
基于上述原因，本论文从缺失数据(无回答)的入手，探讨了缺失数据产生的原因与机制，论述了缺失数据给微观计量带来的影响。
Therefore, the: thesis begins with the data deficiency, discusses the reason and mechanism of data deficiency, and discourses the impact of the data deficiency on micro econometric.
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).
结论多重填补方法可以处理有缺失数据资料中的许多普遍问题，可提高统计效率，尤其是MCMC模型在处理复杂的缺失数据上，优势明显。
Conclusion mi is able to solve a variety of problems in missing data sets and to improve the statistical power, especially with the use of MCMC method, for complicated missing data sets.
介绍分层随机抽样条件下多重插补法处理缺失数据的基本思想，分析可忽略无回答的分层随机抽样建立多重插补的常用方法，并通过实例加以说明。
The paper introduces multiple imputation (mi) for missing data in stratified random sampling, and discusses the ordinary method of mi with ignorable nonresponse, and illustrates the essential steps.
默认的或缺失的数据如何标准化？
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.
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