Nonparametric Curve Estimation: Theory and Practice

非参数曲线估计：理论与实践

• 批准号: 0638468
• 负责人: Sam Efromovich
• 金额: --
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0638468&HistoricalAwards=false
• 关键词: Nonparametric; Curve; Estimation; Theory; Practice

ABSTRACT The proposal focuses on developing statistical theory, methodology and methods of data-driven nonparametric curve estimation motivated by and tested on biological, psychological, medical, and environmental applications, with the main aim to bridge the asymptotic theory and applications. The main intellectual objectives are: (i) For models with indirect observations (like censored and biased regression, hidden components, modeling of time series in the presence of nonparametric trend and volatility) develop the theory of sharp minimax estimation as well as of mimicking of oracles that know direct observations and/or nuisance functions unavailable to the statistician; (ii) For controlled experiments, develop the theory, methodology and methods of sequential sampling and estimation for the case where optimal (under more common criteria) solution depends on estimated and/or nuisance functions; (iii) Develop the theory and methods of a local aggregation of wavelet estimates. Practical problems include the study of municipal wastewater treatment plants, modeling of levels of contaminants and residual disinfectants in Albuquerque water basin, analysis of drinking patterns and behavior change initiation during pregnancy, study of temporal and spatial structures of plants in Sevilleta National Wildlife Refuge, learning machines for recovery magnetic resonance images and the analysis of satellite data. The primary focus of this research is to develop, in collaboration with Sandia and Los Alamos National Laboratories as well as with the UNM Medical and Engineering Schools, algorithms and software for statistical learning and adaptive estimation motivated by and tested on the following environmental, medical and biological applications: Statistical modeling of temporal and spatial structures of plants in Sevilleta National Wildlife Refuge which will allow to study global weather changing; The study of municipal wastewater treatment plants; Modeling and analysis of change points in levels of contaminants and residual disinfectants in Albuquerque water basin with applications to a homeland security drinking water monitoring; Learning machines for recovery magnetic resonance images and the analysis of environmental satellite data including temperature and humidity; Analysis of drinking patterns and behavior change initiation during pregnancy which reduces likelihood of relapses. The broader impact of the research is defined by well--understood applications that will benefit the society and help students and a broader audience to understand the importance of mathematical sciences. The impact will be based on the followingproposed activities: (i) Graduate students will participate in the research; (ii) To promote learning, scientific seminars will be held for undergraduate and graduate students, and talks by the proposer and the students will be presented during the UNM mathematical awareness weeks for high-school students. (iii) To broaden participation of under-represented groups, regular presentations will be held at outreach seminars conducted by the UNM Gallup and Valencia campuses. (iv) The developed software will be freely available. Medical, environmental and biological findings, benefiting the society, will be published in not-technical journals.

摘要 该提案的重点是发展统计理论，方法和数据驱动的非参数曲线估计方法，这些方法受到生物，心理，医学和环境应用的启发和测试，主要目的是弥合渐近理论和应用。主要的智力目标是：（i）对于具有间接观测的模型（如删失和有偏回归，隐藏组件，在非参数趋势和波动性存在下的时间序列建模）发展尖锐的极大极小估计理论以及模仿知道统计学家无法获得的直接观测和/或滋扰函数的预言家;（ii）对于控制实验，发展理论，方法和顺序抽样和估计的情况下，最佳（根据更常见的标准）的解决方案取决于估计和/或滋扰功能的方法;（iii）发展小波估计的局部聚合的理论和方法。实际问题包括城市污水处理厂的研究，污染物和残留消毒剂在阿尔伯克基水流域的水平建模，饮用模式和行为变化的分析在怀孕期间开始，在塞维利亚国家野生动物保护区植物的时间和空间结构的研究，学习机器恢复磁共振图像和卫星数据的分析。 这项研究的主要重点是与桑迪亚和洛斯阿拉莫斯国家实验室以及UNM医学和工程学院合作，开发用于统计学习和自适应估计的算法和软件，这些算法和软件由以下环境，医学和生物应用激发和测试：Sevilleta国家野生动物保护区植物时空结构的统计建模，这将使研究全球天气变化成为可能;城市污水处理厂的研究;在阿尔伯克基流域污染物和残留消毒剂水平的变化点的建模和分析，并应用于国土安全饮用水监测;用于恢复磁共振图像的学习机和环境卫星数据（包括温度和湿度）的分析;分析怀孕期间饮酒模式和行为改变，减少复发的可能性。这项研究的更广泛的影响是由很好理解的应用程序定义的，这些应用程序将使社会受益，并帮助学生和更广泛的受众了解数学科学的重要性。影响将基于以下拟议的活动：（i）研究生将参与研究;（ii）为促进学习，将为本科生和研究生举办科学研讨会，并在新墨西哥大学为高中生举办的数学认识周期间由提议者和学生进行演讲。(iii)为了扩大代表性不足群体的参与，将在新墨西哥大学盖洛普和瓦伦西亚校区举办的外联研讨会上定期介绍情况。(iv)开发的软件将免费提供。有益于社会的医学、环境和生物学研究成果将在非技术性期刊上发表。