Statistical Theory and Methodology

统计理论与方法

• 批准号: 1608182
• 负责人: Bradley Efron
• 金额: $ 70万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1608182&HistoricalAwards=false
• 关键词: Statistical; Theory; Methodology

Modern computational capabilities, modern theory, and the expanded data sets produced by modern scientific equipment have greatly increased the scope of statistical inference. This research project investigates a set of questions in probability and statistics raised by large-scale data collection. While of increasing use, empirical Bayes methods have proved difficult to justify. The approach under development in this project brings a novel application of exponential family theory to the job, with the goal of clarifying how empirical Bayes analyses converge to traditional Bayes methods as sample sizes increase. The project aims to develop empirical Bayes methods that use large-scale parallel data sets, such as those from microarray studies, to improve estimation in situations reporting many small sub-experiments, each of which by itself has low accuracy; and improved Monte Carlo methods for the computer solution of massive optimization problems.Specific topics under investigation in this research project include large-scale empirical Bayes strategies, importance sampling for computer-assisted inference in formerly intractable situations, and a theory of stability assessment for traditional methods of accuracy estimation. Exponential families of probability distributions play a central role in both computation and statistical inference. A particularly stubborn impediment to their use in massive data analyses is the lack of a suitable norming constant in the exponential family density. This research project further develops computational methods based on solutions of appropriate variational problems; a promising application is in the area of graphical models. A second application of exponential families involves efficient deconvolution of datasets to obtain empirical Bayes estimates.

现代计算能力、现代理论和现代科学设备产生的扩展数据集大大增加了统计推断的范围。本研究项目探讨了大规模数据收集所提出的一系列概率和统计问题。虽然越来越多的使用，经验贝叶斯方法已被证明是难以证明。该项目正在开发的方法为这项工作带来了指数族理论的新应用，其目标是澄清经验贝叶斯分析如何随着样本量的增加而收敛于传统贝叶斯方法。该项目旨在开发经验贝叶斯方法，使用大规模平行数据集，如来自微阵列研究的数据集，以改进在报告许多小型子实验的情况下的估计，其中每一个实验本身的准确性很低;和改进的蒙特卡罗方法的计算机解决大规模优化问题。具体的主题正在调查中，在这个研究项目包括大规模的经验贝叶斯策略，重要性抽样的计算机辅助推理，在以前棘手的情况下，和稳定性评估的理论，传统的方法的准确性估计。 概率分布的指数族在计算和统计推断中起着核心作用。在大规模数据分析中使用它们的一个特别顽固的障碍是指数族密度中缺乏合适的赋范常数。该研究项目进一步开发基于适当变分问题的解决方案的计算方法;一个有前途的应用是在图形模型领域。指数族的第二个应用涉及数据集的有效去卷积以获得经验贝叶斯估计。