Statistical Methods for Multilevel Multivariate Functional Studies

描述（由申请人提供）：目前迫切需要调整标准统计方法，并开发用于分析超大数据集的新方法。如果一个数据集引起了非常困难或无法克服的计算问题，那么它就是非常大的 使用现有的计算系统进行标准数据分析。数据集的大小和复杂性的加速增加部分是由于计算和存储能力的增加、新的测量技术、研究设计和研究单元数量的增加。“该提案涉及用于分析新兴类型的非常大的数据集的统计方法，其中非常高维的结果和预测因子，例如图像或密集采样的生物信号，在数百或数千名受试者的多次访问中记录。所提出的方法将描述横截面，纵向和测量误差的变化，在纵向研究中观察到的数据是函数或图像。标量函数/图像回归分析的方法也将解决非常高维预测的情况下。所提出的方法的灵感和应用到非常大的研究睡眠和扩散张量成像（DTI）脑纤维束成像。

DESCRIPTION (provided by applicant): There is an acute and increasing need to adapt standard statistical methods and to develop new approaches for the analysis of very large data sets. A data set is very large if it raises very difficult or insurmountable computational problems for standard data analysis using available computing systems. The accelerated increase in size and complexity of data sets is due in part to increased computational and storage capabilities, new measurement technologies, study designs, and an increasing number of study "units." This proposal is concerned with statistical methods for the analysis of an emerging type of very large data set, where very high dimensional outcomes and predictors, such as images or densely sampled biosignals, are recorded at multiple visits on hundreds or thousands of subjects. The methods proposed will describe the cross-sectional, longitudinal and measurement error variability in longitudinal studies where observed data are functions or images. Methods for scalar on function/image regression analysis will also be addressed for the case of very highly dimensional predictors. The proposed methodology is inspired by and applied to very large studies of sleep and Diffusion Tensor Imaging (DTI) brain tractography.