Directed Regression

有向回归

• 批准号: 0968707
• 负责人: Benjamin Van Roy
• 金额: $ 33万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0968707&HistoricalAwards=false
• 关键词: Directed; Regression

The research objective of this project is to develop efficient regression algorithms for fitting models that are to be used to guide subsequent decisions. Linear regression algorithms will be designed for static models that support repeated decisions as well as for linear time series models that support dynamic decisions. Logistic regression algorithms will be designed to accommodate use of discrete choice data. The algorithms will take as input decision objectives in addition to data and a model specification.If successful, new algorithms resulting from this research project will enhance, relative to more standard regression algorithms, the quality of decisions made using regression models when selected features do not perfectly capture relationships present in systems that generate data.

该项目的研究目标是开发用于指导后续决策的拟合模型的有效回归算法。 将为支持重复决策的静态模型以及支持动态决策的线性时间序列模型设计线性回归算法。 逻辑回归算法将被设计为适应离散选择数据的使用。 该算法将作为输入决策目标，除了数据和模型规范，如果成功，新的算法产生的这个研究项目将提高，相对于更标准的回归算法，使用回归模型的决策质量时，选定的功能不能完美地捕捉系统中存在的关系，生成数据。