New methods for multiple comparison procedures

多重比较程序的新方法

• 批准号: RGPIN-2018-05119
• 负责人: Peng, Jianan
• 金额: $ 1.31万
• 依托单位: Acadia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712598
• 关键词: New; methods; multiple; comparison; procedures

This research program has three areas. The first area will involve false discovery rate (FDR) control related problems for large-scale multiple testing, one of the challenging and central fields in modern statistics. The second area will involve complex monotone dose-response studies for familywise error rate control. The third area will involve statistical classification with confidence under order restriction. In the first area, complex and large-scale multiple testing problems involve various structures. For example, monotone dose-response microarray experiments need to detect trends in gene expressions caused by increasing dose levels of a compound. It is of common interest for many researchers to compare the results of two independent experiments, where each experiment involves hundreds or thousands of hypothesis tests. I will modify my work of false coverage rate (FCR), which is the expected proportion of non-covering constructed intervals, for monotone dose-response microarray experiments to consider directional FDR control. I will consider the control of FCR in dose-response microarray experiments under the prior knowledge that treatments are at least as good as the control. This type of prior information, which is called simple tree ordering, may come from past experience or subject matter knowledge. Using Sun et al. (2006)'s stratified FDR idea I will develop stratified multiple testing procedures and FCR control with heterogeneous multinomial distributions. I will use a nonparametric empirical Bayes framework to identify genes that are differentially expressed in both of two independent experiments. Identification of the minimum effective dose, which is the lowest dose level with an effect that exceeds that of the zero dose, is very important in drug development. In the second area, I will develop a new interval procedure to simultaneously identify the minimum effective dose in each of several groups under monotone dose-response studies through the partitioning principle. The fact that the utilization of order restriction information increases the efficiency of statistical inference is well documented. The third area for this research program will be statistical classification with confidence under order restriction. Many of the methods developed are inspired by real applications and will be applicable to large-scale statistical problems and complex dose-response studies with order restrictions. Statistical classification has many applications in a wide range of fields including medicine, engineering, and social sciences. This proposal will develop new classification rules that exploit the underlying order among mean values by using ideas from order restricted inference. This research program will train both undergraduate and graduate students to help them participate effectively in an information era overwhelmed by big data.

这项研究计划有三个方面。第一个领域将涉及大规模多重检验的错误发现率（FDR）控制相关问题，这是现代统计学中具有挑战性的核心领域之一。第二个领域将涉及复杂的单调剂量-反应研究，用于家族错误率控制。第三个领域将涉及在订单限制下有信心的统计分类。 在第一个领域，复杂和大规模的多重测试问题涉及各种结构。例如，单调剂量反应微阵列实验需要检测由化合物剂量水平增加引起的基因表达趋势。许多研究人员都有兴趣比较两个独立实验的结果，每个实验都涉及数百或数千个假设检验。我将修改我的工作的假覆盖率（FCR），这是预期的比例，非覆盖构建的区间，单调剂量反应微阵列实验考虑定向FDR控制。我将考虑在剂量反应微阵列实验中，在治疗至少与对照一样好的先验知识下，对FCR进行控制。这种类型的先验信息，称为简单树排序，可能来自过去的经验或主题知识。利用Sun等人（2006）的分层FDR思想，我将开发分层多重检验程序和FCR控制与异质多项分布。我将使用一个非参数经验贝叶斯框架，以确定在两个独立的实验差异表达的基因。 最小有效剂量是指效应超过零剂量的最低剂量水平，确定最小有效剂量在药物开发中非常重要。在第二个领域，我将开发一个新的间隔程序，同时确定在单调剂量反应研究下，通过分区原则的几个组中的每一个的最小有效剂量。 事实上，顺序限制信息的利用增加了统计推断的效率是有据可查的。本研究计划的第三个领域将是顺序限制下的置信度统计分类。 许多开发的方法的灵感来自于真实的应用程序，并将适用于大规模的统计问题和复杂的剂量反应研究顺序的限制。统计分类在包括医学、工程和社会科学在内的广泛领域中有许多应用。该建议将开发新的分类规则，利用从顺序限制推理的想法，平均值之间的潜在顺序。该研究计划将培训本科生和研究生，帮助他们有效地参与大数据淹没的信息时代。