Regularized joint estimation of related vector autoregressive models
Regularized joint estimation of related vector autoregressive models
复制标题
DOI：
10.1016/j.csda.2019.05.007
复制
发表时间：
2019-11
影响因子：
1.8
通讯作者：
Andrey Skripnikov;G. Michailidis
中科院分区：
文献类型：
--
作者：
Andrey Skripnikov;G. Michailidis
在许多应用程序中，人们可以访问多个相关主题的高维时间序列数据。一个令人兴奋的应用领域来自神经影像领域，例如从患有特定神经疾病的不同受试者组（病例/对照）获得的大脑功能磁共振成像时间序列数据。讨论了多个相关向量自回归（VAR）模型的正则化联合估计问题，除了常规套索惩罚之外，还利用了群套索惩罚，从而通过模型借用强度来提高估计的统计效率。开发了一个建模框架，它允许相关主题的群体级别和特定主题的影响，使用群体套索惩罚来估计前者。引入了估计程序，其性能在合成数据上进行了说明，并与其他最先进的方法进行了比较。此外，所提出的方法用于静息态功能磁共振成像数据的分析。特别是，利用来自 ADHD-200 全球竞赛存储库的数据，对注意力缺陷多动障碍 (ADHD) 患者（而不是对照组）的大脑区域间时间效应进行了组级描述性分析。
In a number of applications, one has access to high-dimensional time series data on several related subjects. A motivating application area comes from the neuroimaging field, such as brain fMRI time series data, obtained from various groups of subjects (cases/controls) with a specific neurological disorder. The problem of regularized joint estimation of multiple related Vector Autoregressive (VAR) models is discussed, leveraging a group lasso penalty in addition to a regular lasso one, so as to increase statistical efficiency of the estimates by borrowing strength across the models. A modeling framework is developed that it allows for both group-level and subject-specific effects for related subjects, using a group lasso penalty to estimate the former. An estimation procedure is introduced, whose performance is illustrated on synthetic data and compared to other state-of-the-art methods. Moreover, the proposed approach is employed for the analysis of resting state fMRI data. In particular, a group-level descriptive analysis is conducted for brain inter-regional temporal effects of Attention Deficit Hyperactive Disorder (ADHD) patients as opposed to controls, with the data available from the ADHD-200 Global Competition repository.