CAREER: Methodology for Statistical Computing in Massive Datasets: Parallel Approaches to Cluster and MCMC Estimation

职业：海量数据集中的统计计算方法：聚类和 MCMC 估计的并行方法

• 批准号: 0239734
• 负责人: Ranjan Maitra
• 金额: $ 40万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0239734&HistoricalAwards=false
• 关键词: CAREER; Methodology; Statistical; Computing; Massive

CAREER: Methodology for Statistical Computing in Massive Datasets: Parallel Approaches to Cluster and MCMC EstimationDMS 0239734PI: Ranjan MaitraThis project is aimed at developing practical methodology for statistical analysis and estimation in massively sized databases. Because of automated data collection methods, there is nowa surfeit of severely multi-dimensional records. Grouping them into homogeneous clusters to better understand them is a desirable goal in a variety of applications, yet classical statistical methods are computationally infeasible in many cases. I propose to develop parallel methodology for this context. Although the methodology and theory developed will be quite general and for potential use in applications ranging from business, medicine, the environment, software quality assessment, I will conduct the research in the context of three scientific collaborations. The first pertains to theUS Environmental Protection Agency's (EPA) self-reported Toxic Releases Inventory (TRI) databases, where profiling the different facilities in terms of their product, demographic and business information can improve the accuracy of records, as well as better characterize them vis-a-vis their emissions mix. The second project is to assess the reliability of functional Magnetic Resonance Imaging (fMRI) scans, with a view to understanding the cognitive processes of the brain, as a first step to patient care and therapy. The third application is in bioinformatics where the goal is to cluster microarray data and also to analyze two-dimensional proteomic gel images. This will help in isolating genes and understanding their relationship with different disorders. Clustering is in general a very difficult problem, with empirical solutions even for very moderately sized datasets. I propose to develop multi-pass methodologies in several different scenarios. I also propose multi-scale simulation approaches to estimation in a high-dimensional context. One of the biggest challenges faced by simulation methods due to high dimensionality is the low mobility around the space to be traversed because of its vastness. I propose to address this issue by connecting these high-dimensional spaces to lower-dimensional ones (which are significantly smaller) and by using these lower scales to traverse from one corner of the higher-dimensional space to another. A final goal of this five-year plan is to investigate the development and estimation in more complex models for proteomic gel data. Most of the plans proposed will be possible only with a parallel computing interface. This is increasingly critical in a large number of scientific applications, and I propose to simultaneously provide statistics students with the necessary expertise by designing suitably tailored graduate and undergraduate classes.

职业：大规模数据集中的统计计算方法：集群和MCMC估计的并行方法dms 0239734PI: Ranjan maitra该项目旨在开发大规模数据库中统计分析和估计的实用方法。由于自动化的数据收集方法，现在有大量的严重多维记录。在各种应用程序中，将它们分组到同构集群中以更好地理解它们是一个理想的目标，然而，在许多情况下，经典的统计方法在计算上是不可行的。我建议在这种情况下开发并行的方法。虽然所开发的方法和理论将非常通用，并且在商业、医学、环境、软件质量评估等应用中具有潜在的用途，但我将在三个科学合作的背景下进行研究。首先是美国环境保护署（EPA）自我报告的有毒物质排放清单（TRI）数据库，该数据库根据不同设施的产品、人口统计和商业信息对其进行分析，可以提高记录的准确性，并更好地根据其排放组合对其进行表征。第二个项目是评估功能性磁共振成像（fMRI）扫描的可靠性，以期了解大脑的认知过程，作为患者护理和治疗的第一步。第三个应用是生物信息学，其目标是聚集微阵列数据，并分析二维蛋白质组凝胶图像。这将有助于分离基因并了解它们与不同疾病的关系。聚类通常是一个非常困难的问题，即使对于非常中等规模的数据集，也有经验解决方案。我建议在几个不同的场景中开发多通道方法。我还提出了在高维环境中进行估计的多尺度模拟方法。由于高维度，仿真方法面临的最大挑战之一是由于空间的巨大而导致其在被遍历空间周围的低移动性。我建议通过将这些高维空间连接到低维空间（它们要小得多）来解决这个问题，并使用这些低尺度从高维空间的一个角落穿越到另一个角落。这个五年计划的最终目标是研究蛋白质组学凝胶数据的更复杂模型的开发和估计。提出的大多数计划只有在并行计算接口的情况下才有可能实现。这在大量的科学应用中越来越重要，我建议通过设计适当定制的研究生和本科生课程，同时为统计学学生提供必要的专业知识。