Inference strategies with applications and boostrapping

具有应用程序和 boostrapping 的推理策略

• 批准号: 98832-2006
• 负责人: Ahmed, Syed
• 金额: $ 0.87万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2006
• 资助国家: 加拿大
• 起止时间: 2006-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=334342
• 关键词: Inference; strategies; applications; boostrapping

This research is primarily concerned with the development of optimal tools to help in the analysis of complex data.  For example, how can DNA microarray data from various sources be combined, showing the promise of immense possible gains in statistical inferential accuracy?  How can uncertain prior information be incorporated into the current estimation process?  How can uncertainty concerning the appropriate statistical model-estimator to use in representing the data sampling process be dealt with?    These questions, and many more, can be better understood through appropriate statistical inferential tools.   The proposed research  demonstrates well-defined data-basesd estimation techniques. The focus of this research program is on the development and application of statistical inference methodologies which performs better than the existing methods. Statistical inference is the process of reasoning from observed data back to its underlying mechanism. Shrinkage and empirical Bayes methods provide useful techniques for combining data from various sources.  B.  Efron (Newsletter of the Royal Statistical Society January, 1995) predicted that shrinkage and empirical Bayes methodology would be a major area of statistical research for the early 21st century.    More recently, several authors considered a new approach to likelihood that weigh components differentially.  Interestingly, this leads to shrinkage type estimators that also feature prominently in empirical Bayes methodology.   New scientific technology, exemplified by DNA microarrays, has suddenly revived interest in these methods. These techniques will advance knowledge because of their applicability to a larger class of problems thereby extending our ability to solve advanced statistical inference problems in health-care, environmetrics, engineering and social sciences.

这项研究主要关注的是开发最佳工具来帮助分析复杂的数据。例如，如何将来自不同来源的DNA微阵列数据结合起来，从而显示出统计推断准确性的巨大可能性。 如何将不确定的先验信息纳入当前的估计过程？ 如何处理有关用于表示数据抽样过程的适当统计模型估计量的不确定性？ 通过适当的统计推断工具，可以更好地理解这些问题以及更多问题。 拟议的研究表明，定义良好的数据为基础的估计技术。该研究计划的重点是统计推断方法的开发和应用，这些方法比现有方法表现得更好。统计推断是从观察到的数据中推理出其潜在机制的过程。收缩和经验贝叶斯方法提供了有用的技术，从各种来源的数据相结合。B.埃夫隆（通讯的皇家统计学会1995年1月）预测，收缩和经验贝叶斯方法将是一个主要领域的统计研究，为21世纪初世纪。 最近，几位作者考虑了一种新的方法来衡量组件differentially.有趣的是，这导致收缩型估计，也突出了经验贝叶斯方法。 以DNA微阵列为代表的新科学技术突然恢复了人们对这些方法的兴趣。这些技术将推进知识，因为它们适用于更大类的问题，从而扩展我们的能力，解决先进的统计推断问题，在医疗保健，计量学，工程和社会科学。