Nonparametric Likelihood Enhancements for Dependent Data

相关数据的非参数似然增强

• 批准号: 1406747
• 负责人: Daniel Nordman
• 金额: $ 12万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1406747&HistoricalAwards=false
• 关键词: Nonparametric; Likelihood; Enhancements; Dependent; Data

The project aims to develop accurate statistical methodology for correlated data, which applies without stringent assumptions about how data may arise. Current statistical methodology often relies on specifying an adequate model for correlated data, which can be a difficult task, and any inference drawn from a mistaken model can be unreliable. A direct benefit of this research is to provide alternative, model-free tools for statistical inference that are not susceptible to model choice and can advance data analysis in scientific areas such as environmetrics, economics, astronomy, etc., which encounter different forms of complex dependent data. Additionally, climate predictions are increasingly relevant for mitigating natural disasters and planning the use of social/economic resources. Research goals include developing new assessments of regional climate models to understand how scale differences in such models may impact climate forecasts. The research targets development of model-free resampling and nonparametric likelihood methods for different types of dependent data structures, temporally and spatially. Three main research problems are: (1) Investigation of optimal implementations of empirical likelihood for time series (as performance is linked to tuning parameters in currently unknown ways); (2) Study of spatio-temporal resampling methods to assess the concept of "scale" in geophysical and environmental processes, with interest in evaluating regional climate models; (3) Development of new resampling methods for irregularly located spatial observations, based on data-transformations, to advance inference with spatial data. Such nonparametric methods can provide valid inference and assessments of dependence structures under mild distributional assumptions, and such methodology can also be helpful for informing model selection.

该项目旨在为相关数据开发准确的统计方法，该方法适用于没有严格假设数据可能如何产生的情况。 目前的统计方法通常依赖于为相关数据指定适当的模型，这可能是一项艰巨的任务，并且从错误的模型中得出的任何推断都可能是不可靠的。 这项研究的一个直接好处是为统计推断提供了替代的、无模型的工具，这些工具不受模型选择的影响，并且可以推进科学领域的数据分析，如生物计量学、经济学、天文学等，其遇到不同形式的复杂相关数据。 此外，气候预测对于减轻自然灾害和规划社会/经济资源的使用越来越重要。 研究目标包括对区域气候模型进行新的评估，以了解这些模型中的尺度差异如何影响气候预测。 该研究的目标是为不同类型的依赖数据结构，时间和空间的无模型的恢复和非参数似然方法的发展。 主要研究问题有三个：（1）时间序列经验似然估计的最优实现（2）研究时空反演方法，以评估地球物理和环境过程中的“尺度”概念，并对评估区域气候模型感兴趣;（3）在数据转换的基础上，为不规则位置的空间观测开发新的重建方法，以推进空间数据的推理。 这种非参数方法可以在温和的分布假设下提供有效的推断和依赖结构的评估，这种方法也可以有助于为模型选择提供信息。