STATISTICAL SOFTWARE FOR DATA WITH MISSING VALUES

针对缺失值数据的统计软件

• 批准号: 2107957
• 负责人: DEBORAH J DONNELL
• 金额: $ 8.08万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-06-08 至 1995-06-07
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2107957
• 关键词: computer human interaction; computer program /software; computer system design /evaluation; mathematical model; statistics /biometry

In every large scale study, missing data is ubiquitous and inevitable. The overall cost incurred by the lost information is enormous, both financially, as cases are omitted due to incomplete data, and scientifically, as omitting data can lead to biased results. Research statisticians have developed high quality methods for estimating missing data, which can often enable the data analyst to use cases that would otherwise have to be dropped from the study. However, this research is not yet readily available to the practicing analyst, because it is not embedded in software that is both accessible and easy to use. We propose to extend the S-PLUS statistical computing language to allow data analysts to input and manipulate missing data models. We aim to support popular imputation methods, the EM algorithm, Markov Chain Monte Carlo, and multiple imputation methods. Moreover, we will provide an implementation that will permit researchers to experiment more easily with new missing data methods, as the software will be fully extensible.

在每一个大规模的研究中，缺失数据是普遍存在的，也是不可避免的。 信息丢失所带来的总体成本是巨大的， 财务方面，由于数据不完整，案件被忽略， 科学地说，因为省略数据会导致有偏见的结果。 研究 统计学家已经开发出高质量的方法来估计缺失 数据，这通常可以使数据分析师能够使用 否则必须从研究中删除。 然而，这项研究是 还没有现成的执业分析师，因为它不是 嵌入到软件中，既可访问又易于使用。 我们建议扩展S-PLUS统计计算语言， 数据分析师输入和操作缺失的数据模型。 我们的目标是 支持流行的插补方法，EM算法，马尔可夫链Monte Carlo和多重插补方法。 此外，我们将提供一个 这将使研究人员能够更容易地进行实验， 新的缺失数据方法，因为软件将是完全可扩展的。