Directed Regression

这个项目的研究目标是开发高效的回归算法来拟合模型，这些模型将用于指导后续决策。线性回归算法将设计用于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。逻辑回归算法的设计将适应离散选择数据的使用。这些算法除了数据和模型规范外，还将作为输入决策目标。如果成功，该研究项目产生的新算法将相对于更标准的回归算法，在所选特征不能完美地捕获生成数据的系统中存在的关系时，提高使用回归模型做出的决策的质量。

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.