Flexible Statistical Modelling

题目：灵活的统计建模摘要研究者和他的学生将从传统的逻辑回归模型（正则化）的角度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：它可以估计类概率，并且无缝地泛化到多类问题。这种方法和其他一些更简单的方法将被用于基因表达阵列的分类。因此，该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，特别是基因表达阵列。尽管科学期刊上充斥着用于这一时尚领域的外来统计和计算算法，但这位研究者和他的同事们坚信，对经典方法进行一些非常简单的修改，效果也一样好，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.