Mathematical Sciences: A Robust Approach to the Prediction Problem

数学科学：预测问题的稳健方法

• 批准号: 8706044
• 负责人: Paul Horn
• 金额: --
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-07-01 至 1989-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8706044&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Robust; Approach; Prediction

The research from this proposal will create techniques that will enhance the way in which scientists predict responses from limited amounts of data. The techniques will be robust in nature thereby precluding unduly restrictive distributional assumptions. An approach will be taken similar to what has been done with confidence intervals: robust estimates of location and spread will be used to create prediction intervals for a function of a set of future observations. Recent advances in the field of adaptive estimation will also be used to classify the underlying distribution. This research will go far beyond currently available statistical methods for predicting responses. Statistical prediction will be developed for new types of data, types for which existing statistical methods have been used even though scientists knew they were misfit because no other methods existed. Since the ability to predict is vital to scientists and engineers, the results from this research will aid in advancing the state-of-the-art in a variety of fields.

这项提案的研究将创造技术， 将增强科学家预测 有限的数据量。 这些技术在本质上将是稳健的 从而排除了不适当的限制性分配假设。 将采取类似于 置信区间：位置和分布的稳健估计 将用于为函数创建预测区间， 未来的观察。 领域的最新进展 自适应估计也将被用于分类的基础 分布 这项研究将远远超出目前可用的 统计学方法来预测反应。 统计 预测将开发新类型的数据，类型为 现有的统计方法已经使用，即使 科学家们知道他们不适合，因为没有其他方法 存在过 由于预测能力对科学家和 工程师们，这项研究的结果将有助于推进 各种领域的最新技术