SBIR Phase II: Bootstrap Tilting Inference and Large Data Sets

SBIR 第二阶段：Bootstrap 倾斜推理和大数据集

• 批准号: 0078706
• 负责人: Tim Hesterberg
• 金额: $ 39.99万
• 依托单位: Insightful Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-12-01 至 2003-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0078706&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Bootstrap; Tilting

This Small Business Innovation Research Phase II project is for development of fast bootstrap confidence intervals and hypothesis tests, and ways to make bootstrapping feasible for large data sets. Classical inference (intervals and tests) methods are known to be inaccurate when theoretical assumptions are violated, the usual case in practice. For example, skewness causes the usual t-test to be in error. The new methods are an order of magnitude (power of sqrt(n), where n is the sample size) more accurate in general than classical inferences. Bootstrap methods are a promising alternative to classical inferences, and can handle complex statistics including modern robust statistics, but are slow and have been little used in practice.The methods proposed are typically 17--37 times faster than other bootstrap methods. The methods are fast enough to be seamlessly incorporated into standard software, alongside or instead of classical inferences. This provides statistical practitioners a realistic alternative to easy but inaccurate classical inferences and non-robust methods. The competitive advantage to the firm that does this first is a major opportunity. Furthermore, the large sample methods would be attractive in the thriving data mining market.

这个小型企业创新研究第二阶段项目是为了开发快速自举置信区间和假设检验，以及使自举适用于大型数据集的方法。 经典的推理（区间和测试）方法是已知的，是不准确的，当理论假设被违反，在实践中的常见情况。例如，偏度会导致通常的t检验出错。 新的方法是一个数量级（平方幂（n），其中n是样本大小）更准确的一般比经典的推论。 Bootstrap方法是一种很有前途的替代经典推理的方法，可以处理复杂的统计数据，包括现代稳健统计数据，但速度较慢，在实际中应用较少，本文提出的方法通常比其他Bootstrap方法快17- 37倍。 这些方法足够快，可以无缝地集成到标准软件中，与经典推理一起或代替经典推理。 这为统计从业者提供了一种现实的替代方案，可以取代简单但不准确的经典推论和非稳健方法。 首先做到这一点的公司的竞争优势是一个重大的机会。 此外，大样本方法将在蓬勃发展的数据挖掘市场中具有吸引力。