Problems in Statistical Model Building

统计模型构建中的问题

• 批准号: 0072292
• 负责人: Grace Wahba
• 金额: $ 33.52万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-15 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0072292&HistoricalAwards=false
• 关键词: Problems; Statistical; Model; Building

PROJECT ABSTRACTProblems in Statistical Model Building. Grace Wahba, PI.This research is to further the development of Smoothing Spline ANOVA and related variational methods for multivariate function estimation and statistical model building, so that these methods may be used in analyses of very large complex heterogenous data sets as occur in demographic medical studies, environmental and climatic data analyses and classification problems in a variety of areas. These methods are flexible nonparametric methods, but generally contain commonly used parametric families as special cases. The general approach proceeds in the following steps: (i)propose families of models that are appropriate for specific areas of application, (ii) develop new numerical algorithms as required for fitting the models, (ii) develop further methods for tuning the models and providing accuracy estimates, (iii) develop information concerning the properties of the methods, including testing on realistic simulated observations where the `truth' is known, (iv) and applying the resulting methods to important data sets, with the expectation of extracting information from these data that is not obtainable by standard parametric methods.The goal of this project is provide to scientists in medical, environmental and atmospheric sciences and supervised machine learning, new and useful tools to more efficiently analyze their data. Tasks are proposed to develop new methods that are appropriate for more efficient data analysis in complex demographic studies which follow populations over time, collecting information useful for understanding relationships between possible risk factors and the incidence and progression of various diseases. Tasks are also proposed for the development of new methods that are appropriate for understanding relationships among various factors of interest in large environmental and atmospheric data sets with `non-standard' indirect observational data; and for exploiting some new methods in classification that have wide applicability for building classification algorithms based on learning from large data sets in very high dimensional spaces.

项目摘要：统计模型建立中的若干问题。格蕾丝·瓦巴，PI。本研究旨在进一步发展平滑样条方差分析及其相关的变分方法，用于多元函数估计和统计模型的建立，使这些方法可用于人口医学研究、环境和气候数据分析以及各种领域的分类问题的超大复杂异构数据集的分析。这些方法是灵活的非参数方法，但通常包含常用的参数族作为特殊情况。一般做法的步骤如下：(i)提出适合特定应用领域的模型族，（ii）根据拟合模型的需要开发新的数值算法，（ii）开发进一步的方法来调整模型并提供精度估计，（iii）开发有关方法属性的信息，包括在已知“真相”的现实模拟观测中进行测试，（iv）并将结果方法应用于重要数据集。期望从这些数据中提取出标准参数方法无法获得的信息。该项目的目标是为医学、环境和大气科学以及监督机器学习领域的科学家提供新的有用工具，以更有效地分析他们的数据。提出的任务是发展适合于在长期跟踪人口的复杂人口研究中更有效地分析数据的新方法，收集有助于了解可能的危险因素与各种疾病的发病率和进展之间关系的信息。还提出了发展新方法的任务，这些新方法适合于理解具有“非标准”间接观测数据的大型环境和大气数据集中各种感兴趣因素之间的关系；并开发了一些新的分类方法，这些方法广泛适用于构建基于从非常高维空间的大数据集学习的分类算法。