Flexible and Adaptive Statistical Modeling

灵活且自适应的统计建模

• 批准号: 0705007
• 负责人: Robert Tibshirani
• 金额: $ 34.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705007&HistoricalAwards=false
• 关键词: Flexible; Adaptive; Statistical; Modeling

The investigator studies and develops computer-intensive methods for applied statistics, with applicatiion in biology. His current proposal has three projects a) complementary clustering, a kind of orthogonal decomposition analogous to principal compoennts, b) the fused lasso for spatial smoothing and hot-spot detection, c) pre-validation- a method for inference for the pN setting and d) The new edition of the text "The Elements of Statistical Learning"There have been significant developments in the areas of applied regression and classification over the past 10-15 years. Much of the impetus originally came from outside of the field of statistics, from areas such as computer science, machine learning and neural networks. As a result, we now have at our disposal a very powerful collection of techniques for adaptive regression and classification. These are now being 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 practical problems. In this work the investigator plans to develop and study new tools for the important practical problems.

研究者研究和开发应用统计学的计算机密集型方法，并将其应用于生物学。他目前的建议有三个项目a)互补聚类，一种类似于主成分的正交分解，b)用于空间平滑和热点检测的融合套索，c)预验证-一种用于pN设置的推断方法，d)文本的新版本“统计学习的要素”在过去的10-15年中，在应用回归和分类领域取得了重大进展。很多推动力最初来自统计领域之外，比如计算机科学、机器学习和神经网络。因此，我们现在拥有了一套非常强大的自适应回归和分类技术。这些技术现在正被应用于医学诊断、生物信息学和遗传建模、化学过程控制、形状、手写、语音和面部识别、金融建模以及其他广泛的重要实际问题。在这项工作中，研究者计划为重要的实际问题开发和研究新的工具。