SBIR Phase I: Bootstrap Tilting Inference and Large Data Sets

SBIR 第一阶段：Bootstrap 倾斜推理和大数据集

• 批准号: 9861360
• 负责人: Tim Hesterberg
• 金额: $ 10万
• 依托单位: Insightful Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-01-01 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9861360&HistoricalAwards=false
• 关键词: SBIR; Phase; Bootstrap; Tilting; Inference

9861360 This Small Business Innovation Research Phase I project is for research on confidence intervals and hypothesis tests using fast bootstrap methods, and ways to make bootstrapping feasible for large data sets. Classical inference (intervals and tests) methods are known to be inaccurate when the underlying assumptions are violated, the usual case in practice. For example, skewness causes the usual t-test to be in error. The new methods would be an order of magnitude (power of 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 modem robust statistics, but are slow and have been little used in practice. The methods proposed are 17 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. The large sample methods would be attractive in the thriving data mining market.

9861360 这个小型企业创新研究第一阶段项目旨在使用快速引导方法研究置信区间和假设检验，以及使引导对于大型数据集可行的方法。众所周知，当违反基本假设时（实践中的常见情况），经典推理（区间和检验）方法是不准确的。例如，偏度会导致通常的 t 检验出错。一般来说，新方法比经典推论更准确一个数量级（n 的幂，其中 n 是样本大小）。 Bootstrap 方法是经典推理的一种有前途的替代方法，可以处理复杂的统计数据，包括现代的鲁棒统计数据，但速度很慢并且在实践中很少使用。所提出的方法比其他引导方法快 17 倍。 这些方法足够快，可以无缝地融入标准软件中，与经典推论一起或代替经典推论。这为统计从业者提供了一种现实的替代方案，可以替代简单但不准确的经典推论和非稳健方法。对于首先这样做的公司来说，竞争优势是一个重大机会。 大样本方法在蓬勃发展的数据挖掘市场中将具有吸引力。