Topics in multiple comparison procedures and variable selection

多重比较过程和变量选择的主题

• 批准号: 261301-2013
• 负责人: Peng, Jianan
• 金额: $ 1.09万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635742
• 关键词: Topics; multiple; comparison; procedures; variable

This research program has three areas. The first area will involve low dose risk estimation via simultaneous inference. The second area will involve dose-response microarray analysis and sample size determination for false discovery rate control. The third area will involve robust variable selection. It is a well-known concern in quantitative risk analysis that lack of low dose information or poor application of any limited low dose data can lead to unreliable inferences. I will use the three-segment confidence bands instead of the currently used hyperbolic bands to construct sharper lower confidence bound on the benchmark dose associated with a particular risk for both nonquantal data and quantal data. One goal of dose-response microarray experiments is to detect trends in gene expressions caused by increasing dose levels of a compound. I will modify my earlier work on traditional monotone dose-response studies to dose-response microarray experiments. I will tackle the challenging multiplicity adjustment problem in dose-response microarray experiments. I will also consider sample size determination for false discovery control in treatments with a control analysis for microarray studies. The penalized least squares method with some appropriately defined penalty is widely used for simultaneous variable selection and coefficient estimation in regression. However, the efficiency of the penalized least squares method is adversely affected by outlying observations and heavy-tailed distributions. I will consider rank-based nonlinear model via penalization and robust variable selection based on the combined penalization in the statistical literature for high dimensional data. The new methods proposed in the first area will provide more accurate results in enviromental risk assessment. The results in the second area will be very useful for new drug development and microarray experiment design. The results in the third area will enrich variable selection research. This research program emphasizes the development and dissemination of new statistical methods and provides many opportunities to train highly qualified personnel.

这项研究计划有三个方面。第一个领域将涉及通过同步推断进行低剂量风险估计。第二个领域将涉及剂量反应微阵列分析和样本大小确定错误发现率控制。第三个领域将涉及稳健的变量选择。在定量风险分析中，一个众所周知的问题是，缺乏低剂量信息或任何有限的低剂量数据应用不当可能导致不可靠的推断。我将使用三段置信带而不是目前使用的双曲线带来构建与非量子数据和量子数据的特定风险相关的基准剂量的更清晰的置信下限。剂量反应微阵列实验的一个目标是检测由化合物剂量水平增加引起的基因表达趋势。我将修改我的早期工作，传统的单调剂量反应研究的剂量反应微阵列实验。我将解决剂量反应微阵列实验中具有挑战性的多重性调整问题。我还将考虑在微阵列研究的对照分析治疗中确定假发现控制的样本量。惩罚最小二乘法是一种同时进行变量选择和系数估计的方法。然而，惩罚最小二乘方法的效率受到离群观测值和重尾分布的不利影响。我将考虑基于秩的非线性模型通过惩罚和稳健的变量选择的基础上的组合惩罚在高维数据的统计文献。在第一个领域中提出的新方法将为环境风险评价提供更准确的结果。第二个方面的研究结果将对新药开发和微阵列实验设计非常有用。第三方面的研究成果将丰富变量选择的研究。该研究计划强调新的统计方法的开发和传播，并提供了许多培训高素质人才的机会。