Flexible and Adaptive Statistical Modeling

灵活且自适应的统计建模

• 批准号: 0404594
• 负责人: Robert Tibshirani
• 金额: --
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0404594&HistoricalAwards=false
• 关键词: Flexible; Adaptive; Statistical; Modeling

In the past 10-15 years there have been significant developments in theareas of applied regression and classification. Much of the impetusoriginally came from outside of the field of statistics, from areassuch as computer science, machine learning and neural networks. In somecases, statisticians have helped to synthesize these innovations, often byreinterpreting them from the point of view of standard statistical models.As a result, we now have at our disposal a very powerful collection oftechniques for adaptive regression and classification. These are nowbeing applied to medical diagnosis, bioinformatics and genetic modeling,chemical process control, shape, handwriting, speech and face recognition,financial modeling, and a wide range of other important practicalproblems. In this proposal, the investigator develops new methods for adaptive regression and classification, with particular applicationto problems in bioinformatics and genomics. One such method is the"fused lasso", a technique which seeks solutions which are both sparseand and smooth. It is designed for problems in which the features havea natural ordering, e.g. in time or space. Protein mass spectometryrepresents one such area of application.In this research, the investigator develops new statistical algorithmsfor analyzing data from biological and human experiments. This workis part of the exploding field of "bioinformatics", which seeks to makesense of the huge volume of data produced by new technologies in biologyand medicine. This kind of work represents the next step after thesequencing of the human genome, and has enormous potential for societalimpact through disease prevention and treatment.

在过去的10-15年里，应用回归和分类领域有了重大的发展。大部分动力最初来自统计学领域之外，来自计算机科学，机器学习和神经网络等领域。在某些情况下，统计学家帮助综合了这些创新，通常是从标准统计模型的角度重新解释它们。结果，我们现在拥有了一系列非常强大的自适应回归和分类技术。 这些现在被应用于医疗诊断，生物信息学和遗传建模，化学过程控制，形状，笔迹，语音和面部识别，金融建模以及其他广泛的重要实际问题。 在该提案中，研究人员开发了自适应回归和分类的新方法，特别适用于生物信息学和基因组学中的问题。 其中一种方法是“融合套索”，这是一种寻求稀疏且平滑的解决方案的技术。它是为解决特征具有自然顺序的问题而设计的，例如在时间或空间上。蛋白质质量计量学就是这样一个应用领域。在这项研究中，研究人员开发了新的统计算法，用于分析生物和人体实验的数据。 这项工作是“生物信息学”爆炸性领域的一部分，该领域试图弄清楚生物学和医学新技术产生的大量数据。这类工作代表了人类基因组测序之后的下一步，并且通过疾病预防和治疗具有巨大的社会影响潜力。