Least Angle Regression

最小角回归

• 批准号: 6933500
• 负责人: TIM C HESTERBERG
• 金额: $ 9.97万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-15 至 2006-05-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6933500
• 关键词: clinical research; computational biology; computer graphics /printing; computer human interaction; computer program /software; data collection methodology /evaluation; data quality /integrity; human data; mathematical model; model design /development; statistics /biometry; technology /technique development

DESCRIPTION (provided by applicant): This SBIR project aims to produce superior methods and software for classification and regression when there are many potential predictor variables to choose from. The methods should (1) produce stable results, where small changes in the data do to produce major changes in the variables selected or in model predictions, (2) produce accurate predictions, (3) facilitate scientific interpretation, by selecting a smaller subset of predictors which provide the best predictions, (4) allow continuous and categorical variables, and (5) support linear regression, logistic regression (predicting a binary outcome), survival analysis, and other types of regression. This project is based on least angle regression, which unifies and provides a fast implementation for a number of modern regression techniques. Least angle regression has great potential, but the state of the art is limited to linear regression with continuous or binary variables, and uses numerically-unstable calculations. The outcome of this project should be software which is more robust and widely applicable. This software would apply broadly, including to medical diagnosis, detecting cancer, feature selection in microarrays, and modeling patient characteristics like blood pressure. Phase I work will demonstrate feasibility by extending least angle work in three key directions-categorical predictors, logistic regression, and a numerically-accurate implementation. Phase II will extend the work to other types of explanatory variables (e.g. polynomial or spline functions, and interactions between variables), and to survival and other additional regression models. This proposed software will enable medical researchers to obtain high prediction accuracy, and obtain stable and interpretable results, in high-dimensional situations.

描述（由申请人提供）：该SBIR项目旨在在有许多潜在的预测变量可供选择时生成出色的方法和软件，以进行分类和回归。 The methods should (1) produce stable results, where small changes in the data do to produce major changes in the variables selected or in model predictions, (2) produce accurate predictions, (3) facilitate scientific interpretation, by selecting a smaller subset of predictors which provide the best predictions, (4) allow continuous and categorical variables, and (5) support linear regression, logistic regression (predicting a binary outcome), survival analysis, and other types of回归。该项目基于最小角度回归，该回归统一并为许多现代回归技术提供了快速实施。最小角度回归具有巨大的潜力，但是技术的状态仅限于具有连续或二进制变量的线性回归，并使用数值不稳定的计算。该项目的结果应该是更强大且广泛适用的软件。该软件将广泛应用，包括医学诊断，检测癌症，在微阵列中的特征选择以及对患者特征（如血压）进行建模。 第一阶段的工作将通过在三个关键方向类别预测变量，逻辑回归和数值确定性实现中扩展角度工作来证明可行性。第二阶段将将工作扩展到其他类型的解释变量（例如多项式或样条函数以及变量之间的相互作用），以及生存和其他其他回归模型。该提出的软件将使医学研究人员能够在高维情况下获得高预测准确性，并获得稳定的可解释结果。