Flexible Statistical Methods for Complex Structured Data Analysis

复杂结构化数据分析的灵活统计方法

• 批准号: 312363-2013
• 负责人: He, Wenqing
• 金额: $ 1.09万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571729
• 关键词: Flexible; Statistical; Methods; Complex; Structured

Complex structured data emerge from many fields including medical, industrial and environmental studies. Consequently, there is an urgent need for developing powerful statistical methods to enable more efficient use of information, and to analyze data using more flexible statistical models. One of the most challenging statistical issues is identification of the relationship between outcomes and risk factors, and ultilization of the resulting models for outcome prediction. Extensive research attention has been directed to this area and there has been a rapid growth in the literature using either semi-parametric or nonparametric approaches, but flexible approaches to incorporate various types of risk effects are still in great demand. The primary objective of this research is to develop coherent and novel methodology to deal with data with complex structures. I intend to contribute to this area by employing flexible kernel machine based methods, to develop new methods to facilitate complex relationships between a response and covariates, and to construct predictive models for either single or multiple outcome data. Variable selection methods will be developed to accommodate high dimensional covariate data. The kernel machine based approach allows a more flexible relationship than commonly used linear relationships. Furthermore, the implementation of this method can be made computationally efficient. The predictive models to be developed based on the kernel machine approaches will enjoy better prediction accuracy than existing models.

复杂的结构化数据出现在许多领域，包括医疗，工业和环境研究。因此，迫切需要开发强大的统计方法，以便更有效地利用信息，并使用更灵活的统计模型分析数据。最具挑战性的统计问题之一是识别结果和风险因素之间的关系，并利用所得模型进行结果预测。广泛的研究注意力已经指向这一领域，并已在文献中使用半参数或非参数的方法，但灵活的方法，将各种类型的风险影响仍然是很大的需求。 本研究的主要目标是开发连贯和新颖的方法来处理具有复杂结构的数据。我打算通过采用灵活的基于内核机器的方法来为这一领域做出贡献，开发新的方法来促进响应和协变量之间的复杂关系，并为单个或多个结果数据构建预测模型。 将开发变量选择方法以适应高维协变量数据。基于内核机器的方法允许比通常使用的线性关系更灵活的关系。此外，该方法的实现可以在计算上高效。基于核机器方法开发的预测模型将比现有模型具有更好的预测精度。