Hector Emily C, Reich Brian J, Eloyan Ani
Department of Statistics, North Carolina State University, Raleigh, NC 27695, United States.
Department of Biostatistics, Brown University, Providence, RI 02912, United States.
Biometrics. 2025 Jan 7;81(1). doi: 10.1093/biomtc/ujae159.
Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework's backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain. Statistical and computational properties of our distributed approach are investigated theoretically and in simulations. The proposed approach is used to extract new insights into autism spectrum disorder from the autism brain imaging data exchange.
受神经影像学研究中对计算上易于处理的空间方法的需求驱动，我们开发了一个分布式集成框架，用于估计和推断具有超高维似然性的高斯过程模型参数。我们提出了一种从整体数据视角到局部数据视角的转变，这种转变源于分布式模型构建以及集成估计和推断。该框架的核心是一个计算和统计效率高的集成过程，它在递归划分的空间域中同时纳入了空间分辨率内和空间分辨率之间的依赖性。我们从理论和模拟两方面研究了分布式方法的统计和计算特性。所提出的方法被用于从自闭症大脑成像数据交换中提取关于自闭症谱系障碍的新见解。
