Flexible Statistical Modelling

灵活的统计建模

• 批准号: 0204612
• 负责人: Trevor Hastie
• 金额: $ 23.8万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0204612&HistoricalAwards=false
• 关键词: Flexible; Statistical; Modelling

Proposal ID: DMS-0204612PI: Trevor HastieTitle: Flexible statistical modelingAbstractThe investigator and his students will study the popular support vector machines from the viewpoint of the traditional logistic regression model (with regularization). While the latter exhibits very similar generalization performance to the SVM, it has several advantages: it estimates class probabilities, and generalizes to multiclass problems seamlessly. This and several other even simpler approaches will be developed for the classification of gene expression arrays.This proposal will thus develop useful models for making class predictions in a variety of high-dimensional situations, in particular for gene expression arrays. While the science journals abound with exotic statistical and computational algorithms for use in this fashionable domain, this investigator and his colleagues firmly believe that some very simple modifications of classical approaches perform as well or better, and are easier to understand.

提案ID：DMS-0204612 PI：特雷弗Hastie标题：灵活的统计建模摘要研究者和他的学生将从传统逻辑回归模型（带正则化）的角度研究流行的支持向量机。虽然后者表现出与SVM非常相似的泛化性能，但它有几个优点：它估计类概率，并无缝地推广到多类问题。这个和其他几个更简单的方法将被开发用于基因表达阵列的分类，因此，这个建议将开发有用的模型，用于在各种高维情况下进行类预测，特别是对于基因表达阵列。虽然科学期刊上充斥着用于这个时髦领域的奇异统计和计算算法，但这位研究人员和他的同事坚信，经典方法的一些非常简单的修改表现得一样好或更好，并且更容易理解。