Flexible Statistical Modelling

提案ID：DMS-0204612PI：Trevor Hastie标题：灵活的统计建模摘要研究人员和他的学生将从传统的Logistic回归模型(正则化)的观点来研究流行的支持向量机。虽然后者表现出与支持向量机非常相似的泛化性能，但它有几个优点：它估计类别概率，并无缝地推广到多类问题。这种方法和其他几种更简单的方法将被开发用于基因表达阵列的分类。因此，这一建议将开发有用的模型，用于在各种高维情况下进行类别预测，特别是对于基因表达阵列。虽然科学期刊上充斥着在这个时髦领域使用的奇异统计和计算算法，但这位研究人员和他的同事们坚信，对经典方法进行一些非常简单的修改，效果与经典方法一样好，甚至更好，而且更容易理解。

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.