STATISTICAL METHODS FOR RECURSIVELY PARTITIONED TREES

递归分区树的统计方法

• 批准号: 6387141
• 负责人: WILLIAM D. SHANNON
• 金额: $ 16.34万
• 依托单位: BARNES-JEWISH HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2003-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6387141
• 关键词: classification; computer assisted medical decision making; computer program /software; computer simulation; experimental designs; human data; information system analysis; mathematical model; model design /development; statistics /biometry

DESCRIPTION (Adapted from the Applicant's Abstract): This proposed project has three primary objectives. Objective 1 is to develop improved strategies for fitting more accurate classification and regression tree (i.e., CART) models. Objective 2 is to develop a formal framework to allow statistical inference on tree models. Objective 3 is to develop and distribute public-domain software that will allow applied data analysts to implement the methods we develop in the first two objectives. To meet these objectives we will integrate statistical and computational machine learning approaches. We believe our work can have a significant impact in biomedical data analysis by combining the strengths of statistics for developing objective criteria for model selection and for providing a framework for assessing and quantifying uncertainty associated with a model, with the strengths of machine learning for fitting models to large and complex datasets.

描述（改编自申请人的摘要）：该拟议项目具有 三个主要目标。目标1是制定更好的战略， 拟合更精确的分类和回归树（即，CART）模型。 目标2是制定一个正式框架，允许对以下方面进行统计推断： 树模型目标3是开发和分发公共领域软件 这将允许应用数据分析师实施我们开发的方法， 前两个目标。为了实现这些目标，我们将 统计和计算机器学习方法。我们相信我们的工作 可以通过结合生物医学数据分析， 统计数据在制定模型选择客观标准方面的优势 并为评估和量化不确定性提供框架 与模型相关联，与机器学习的优势相结合， 模型到大型复杂数据集。