Inference under Selection and Model Uncertainty

选择和模型不确定性下的推理

• 批准号: 1105127
• 负责人: Linda Young
• 金额: $ 17.24万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1105127&HistoricalAwards=false
• 关键词: Inference; under; Selection; Model; Uncertainty

This project has two distinct parts, each suggested by problems of inference in genomic experiments. The first problem arises because, typically, thousands of genes are screened, and a smaller number are selected for further study. Statistical inference must take this selection mechanism into account, otherwise the actual confidence coefficient is smaller than the nominal level, and approaches zero as the number of genes increases. The goal is to construct valid frequentist confidence intervals for the means of the selected populations. This will provide a confidence interval alternative to the False Discovery Rate. The second problem deals with inference under model uncertainty, where the goal is to account for the variability induced by the collection of models. Here a Bayesian approach is taken, seeking to construct intervals accounting for model uncertainty, investigate the impact of the choice of priors on model space, and construct new search algorithms that take advantage of parallel processing and can be used in the case when there are more covariates than observations.The work will have impact in both genomic studies and high performance computing. First, for inference from genomic studies, a valid statistical procedure to screen results will be provided. Insuring that the inferences are valid is of crucial importance, as illustrated by a recent NY Times article where a genomic disease therapy was found to be useless, because of faulty statistical inference (``How Bright Promise in Cancer Testing Fell Apart", NY Times, July 7, 2011). Second, parallel processing algorithms, using high performance computing, will be developed. These algorithms take advantage of the abundance of processors typically available, and split the large genomic selection problem across the many processors. This results in answers from these statistical procedures that can be available in real time, and thus be relevant in a clinical setting.

该项目有两个不同的部分，每个部分都由基因组实验中的推断问题提出。 出现第一个问题是因为通常筛选了成千上万个基因，并且选择了较小的基因进行进一步研究。 统计推断必须考虑此选择机制，否则实际的置信系数小于标称水平，并且随着基因数量的增加而接近零。目标是为所选人群构建有效的频繁置信区间。这将提供错误发现率的置信区间替代方案。 第二个问题涉及模型不确定性下的推论，目标是考虑到模型收集所引起的变异性。 在这里采用了贝叶斯方法，试图构建时间间隔，以说明模型不确定性，研究先验对模型空间的影响，并构建利用并行处理的新搜索算法，并且在观察结果中可以使用比观测更大的情况。在基因组研究和高度性能计算中都有影响。 首先，对于基因组研究的推论，将提供有效的筛选结果统计程序。正如最近的《纽约时报》（Ny Times）的一篇文章所说明的那样，确保这些推论是至关重要的，在该文章中发现了基因组疾病疗法是毫无用处的，因为统计上的统计学不良（````````'ny Times，2011年7月7日，2011年7月7日的癌症测试有多崩溃了）。其次，使用了这些典型的algorith comporting。将大型基因组选择问题划分为许多处理器。