Nonparametric likelihood for dependent data

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。调查人员旨在为依赖或相关的数据结构开发有效的非参数似然和恢复方法。 该项目特别针对非标准抽样设计下的空间数据的经验似然方法的发展，调查非平稳时间和空间设置中的引导的最佳实施，并通过旨在削弱数据相关性的数据转换创建新的恢复方法。 这项工作旨在为几个相关的数据结构提供有效和准确的统计方法，这些方法在没有对基本数据生成过程进行严格假设的情况下适用。科学调查通常依赖于数据的统计分析，这些数据通常以复杂的方式相互关联。 当前的统计方法学在很大程度上依赖于选择概率模型来准确地表示数据中的潜在相关性。 然而，这种模型的选择在实践中可能很困难，从错误的模型中得出的任何推论都可能产生误导。 这项研究的目标是开发替代的，无模型的工具，用于相关数据的有效统计推断，这些工具不容易受到模型选择错误的影响，也可以帮助推进环境计量学，经济学，地质学，天文学等科学领域的数据推断， 自然会遇到相关的数据。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The investigator seeks to develop effective nonparametric likelihood and resampling methods for dependent or correlated data structures. The project particularly targets the development of empirical likelihood methodology for spatial data under nonstandard sampling designs, the investigation of optimal implementation of the bootstrap in non-stationary temporal and spatial settings, and the creation of novel resampling methods through data-transformations aimed at weakening data correlation. This work intends to produce efficient and accurate statistical methodology for several correlated data structures, which are applicable without stringent assumptions on the underlying data-generating process.Scientific investigations commonly rely on the statistical analysis of data, which are often correlated in a complex manner. Current statistical metholodogy depends heavily on selecting probability models to accurately represent the underlying correlation in data. However, such model selection can be difficult in practice and any inference drawn from a mistaken model may be misleading. This research targets developing alternative, model-free tools for valid statistical inference with correlated data, which are not susceptible to errors in model choice and can also help advance data inference in scientific areas such as Environmetrics, Economics, Geology, Astronomy, etc., that naturally encounter correlated data.