Variable Selection in Joint Modelling of Longitudinal and Survival Data and Multi-Response Optimization in Designed Experiments

纵向和生存数据联合建模中的变量选择以及设计实验中的多响应优化

• 批准号: RGPIN-2015-04603
• 负责人: MulayathVariyath, Asokan
• 金额: $ 0.8万
• 依托单位: Memorial University of Newfoundland
• 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=635154
• 关键词: Variable; Selection; Joint; Modelling; Longitudinal

Advances in information technology have enabled researchers to deal with large data sets with high dimensions routinely and variable selection is hence an essential step in modeling these data sets. Classical variable selection approaches are often implemented after evaluating all possible sub-models. They are computationally intensive and become infeasible with high dimensional data. A class of methods via regularization has been developed to target such applications, and they have been lauded for their computational efficiency and stability. We propose two different interesting research components, which are similar in terms of statistical methodology.

信息技术的进步使研究人员能够定期处理高维度的大型数据集，因此变量选择是对这些数据集进行建模的重要步骤。经典的变量选择方法通常是在评估所有可能的子模型后实施的。它们的计算量很大，并且对于高维数据来说不可行。人们已经开发了一类通过正则化的方法来针对此类应用，并且它们因其计算效率和稳定性而受到称赞。我们提出了两个不同的有趣的研究组成部分，它们在统计方法方面相似。