APPLYING RESAMPLING TECHNIQUES TO LARGE DATA SETS

将重采样技术应用于大型数据集

• 批准号: 2790415
• 负责人: ALBERT F ANDERSON
• 金额: $ 9.83万
• 依托单位: PUBLIC DATA QUERIES, INC.
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-01-07 至 1999-07-06
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2790415
• 关键词: computer data analysis; data collection methodology /evaluation; mathematical model; method development; population survey; statistics /biometry

This project will investigate the feasibility and merit of applying bootstrapping and similar resampling strategies to the analysis of relatively large census and survey microdata files. While bootstrapping has in general been applied most fruitfully to small sample research designs, new technology now allows resampling and bootstrapping to be effectively applied to much larger data sets than have been previously analyzed using the techniques. In particular, we will focus on two aims: 1. determining confidence intervals for frequency counts, percentages, and summary statistics for basic multivariate analyses from large census and survey data files; and 2. assessing the potential for resampling techniques to assist in masking sensitive information extracted from data sets in which confidentiality of the respondents (disclosure avoidance) is an important concern and where minimal perturbing of the data is desired. A computational tool utilizing an existing parallel high performance computing environment and optimized for resampling will be created to facilitate the implementation and testing of resampling techniques such as bootstrapping on data sets of 10,000-50,000 records. PROPOSED COMMERCIAL APPLICATION: Incorporating resampling into our own information system, PDQ- Explore, will increase its value. Licensing the software to other information providers will broaden our customer base.

本项目将调查将自举和类似的再分析战略应用于分析相对较大的普查和调查微观数据文件的可行性和优点。虽然自举一般已被最富有成效地应用于小样本的研究设计，新技术，现在允许restrobe和自举被有效地应用到更大的数据集比以前使用的技术分析。我们将特别关注两个目标：1。从大型人口普查和调查数据文件中确定用于基本多变量分析的频率计数、百分比和汇总统计量的置信区间;以及2.评估是否有可能采用重新加密技术，以协助掩盖从数据集中提取的敏感信息，在这些数据集中，答复者的保密性（避免披露）是一个重要问题，希望对数据的干扰最小。将创建一个计算工具，利用现有的并行高性能计算环境，并为恢复进行优化，以促进恢复技术的实施和测试，例如在10，000 - 50，000个记录的数据集上进行引导。建议的商业应用：将Resume扩展到我们自己的信息系统PDQ- Explore中，将增加其价值。将软件授权给其他信息提供商将扩大我们的客户群。