Expanding the Computational Statistics Toolbox for General Hierarchical Models

扩展通用分层模型的计算统计工具箱

• 批准号: 1622444
• 负责人: Perry de Valpine
• 金额: $ 19.99万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622444&HistoricalAwards=false
• 关键词: Expanding; Computational; Statistics; Toolbox; General

Hierarchical statistical models allow analysis of patterns in complex data while accounting for relationships such as temporal or spatial patterns or shared sampling units. A great variety of analysis algorithms for hierarchical models have been developed by statistical researchers but are unavailable to practitioners such as social scientists and biologists. The NIMBLE software platform was developed to bridge this gap and make it easier for scientists to use a variety of algorithms on their specific datasets. In particular NIMBLE provides a programming environment in which researchers can implement algorithms that can then be easily used by others in the context of specific datasets. The work under this project will extend NIMBLE to provide computational methods for working with very flexible statistical methods known as Bayesian nonparametric methods. These methods allow researchers to summarize variables and quantify relationships between different variables in an analysis while making fewer assumptions than standard statistical approaches. While Bayesian nonparametric methods have developed substantially in the last 10-15 years, many of these methods are hard or time-consuming for those working with data to implement on their own. This project will implement many such methods in the NIMBLE software, thereby providing them to practitioners to use in their day-to-day analyses. Moreover, it will provide a foundation for ongoing development and sharing of new and improved such methods in the future.A large amount of research aims to improve the intertwined statistical and computational methods for analysis of hierarchical statistical models. Such research is important because problem-specific hierarchical models facilitate rapid advances in many scientific fields. However, statistical researchers have lacked a flexible software platform designed for programming and disseminating the many varieties of algorithms such as Markov chain Monte Carlo, sequential Monte Carlo, and methods that build upon them. The NIMBLE system provides such a software platform. This project helps to further fill that gap by extending the NIMBLE system to enable use of Bayesian nonparametric methods, with a focus on nonparametric mixture models, of which the Dirichlet process model and related models are widely-known. This extension will allow routine application of these nonparametric mixture models as prior distributions for parts of arbitrary hierarchical models. The project will implement a variety of techniques for fitting Bayesian nonparametric mixtures, focusing on both collapsed and blocked samplers in Markov chain Monte Carlo algorithms. Such techniques methods have been highly developed by specialists but are limited in their research and scientific applications by lack of general implementation.

分层统计模型允许分析复杂数据中的模式，同时考虑时间或空间模式或共享抽样单元等关系。统计研究人员已经开发了针对层次模型的各种分析算法，但对社会科学家和生物学家等从业者无法获得。开发了敏捷的软件平台来弥合这一差距，并使科学家更容易在其特定数据集上使用各种算法。特别是Nimble提供了一个编程环境，研究人员可以在其中实施算法，然后在特定数据集的背景下其他人轻松使用这些算法。 该项目下的工作将扩展敏捷，以提供计算方法，以使用称为贝叶斯非参数方法的非常灵活的统计方法。这些方法使研究人员可以总结变量并量化分析中不同变量之间的关系，同时做出比标准统计方法更少的假设。尽管在过去的10 - 15年中，贝叶斯非参数方法已经大大发展，但其中许多方法对于使用数据工作的人来说很难或耗时。该项目将在敏捷软件中实施许多此类方法，从而向他们提供给从业者进行日常分析。此外，它将为将来的新方法和改进的新方法提供基础。一项大量研究旨在改善交织在一起的统计和计算方法，用于分析层次统计模型。这样的研究很重要，因为特定问题的分层模型促进了许多科学领域的快速发展。但是，统计研究人员缺乏一个灵活的软件平台，旨在编程和传播许多算法的品种，例如马尔可夫链蒙特卡洛，顺序蒙特卡洛，以及基于它们的方法。灵活的系统提供了这样的软件平台。该项目通过扩展敏捷系统来实现贝叶斯非参数方法，重点关注非参数混合模型，从而有助于进一步填补这一空白。该扩展将允许这些非参数混合模型作为任意分层模型部分的先验分布。该项目将实施各种适合贝叶斯非参数混合物的技术，重点是马尔可夫链蒙特卡洛算法中的崩溃和阻塞采样器。这种技术方法已经由专家高度发展，但由于缺乏一般实施而在研究和科学应用中受到限制。