Flexible and Adaptive Statistical Modeling

灵活且自适应的统计建模

• 批准号: 9971405
• 负责人: Robert Tibshirani
• 金额: $ 49.73万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9971405&HistoricalAwards=false
• 关键词: Flexible; Adaptive; Statistical; Modeling

9971405During the 1980's, researchers who were trying to model learning in the brain developed several novel learning algorithms for multilayer non-linear neural networks. Somewhat incidentally, these algorithms turn out to be very powerful techniques for adaptive regression and classification, and have proven their usefulness independent of whether or not they are a good model for the brain. They are now being applied to medical diagnosis, chemical process control, shape recognition and a wide range of other important practical problems. At the same time, there have been significant advances in adaptive techniques in the field of statistics. The new statistical methods are more powerful than classical techniques such as linear regression and linear discriminant analysis. Some of the recently developed procedures include CART (Classification And Regression Trees), generalized additive models, MARS (Multivariate Additive Regression Splines), and sophisticated versions of nearest neighbor algorithms that learn an appropriate metric for the input space. Although they come from different fields using different terminologies, these methods have much in common. One item in this proposal is a research monograph that seeks to bring many of these ideas under one umbrella, explaining them in a unified fashion. Two other items explore some recent new methods for improving classifiers and for adaptive model selection.This work aims at developing new models and methods for making predictions based on historical data. These techniques are important in many different fields including medical diagnosis, financial forecasting and industrial process control. The area of biotechnology is an especially important application for these methods. Scientists now have techniques for measuring gene expression levels for thousands of genes at the same time, allowing the exciting possibility of determining which human genes are involved in a diseases such as cancer and heart disease. Sorting through the mass of information is like trying to find a needle in a haystack, and predictive methods like the ones studied here will provide an important tool in this search.

在20世纪80年代的S期间，试图建立大脑学习模型的研究人员为多层非线性神经网络开发了几种新的学习算法。顺便说一句，这些算法被证明是非常强大的自适应回归和分类技术，并证明了它们的有用性，无论它们是否是大脑的好模型。它们现在被应用于医疗诊断、化工过程控制、形状识别和其他一系列重要的实际问题。与此同时，在统计领域的适应技术方面也取得了重大进展。新的统计方法比线性回归和线性判别分析等经典方法更强大。最近开发的一些过程包括CART(分类和回归树)、广义加法模型、MARS(多元加性回归样条法)和复杂版本的最近邻算法，这些算法为输入空间学习适当的度量。尽管它们来自不同的领域，使用不同的术语，但这些方法有许多共同之处。这项提案中的一项是一本研究专著，试图将这些想法集中在一个保护伞下，以统一的方式解释它们。另外两个项目探索了一些最近改进分类器和自适应模型选择的新方法。这项工作旨在开发基于历史数据进行预测的新模型和方法。这些技术在许多不同的领域都很重要，包括医疗诊断、金融预测和工业过程控制。生物技术领域是这些方法的一个特别重要的应用。科学家们现在有了同时测量数千个基因的基因表达水平的技术，这使得确定哪些人类基因与癌症和心脏病等疾病有关成为可能。在海量信息中进行分类就像大海捞针，像这里研究的预测方法将在这次搜索中提供重要的工具。