A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis.

ABSTRACT: This paper is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multi-level nested correlations. We develop a divide-and-conquer procedure implemented in a fully distributed and parallelized computational scheme for statistical estimation and inference of regression parameters. Despite significant efforts in the literature, the computational bottleneck associated with high-dimensional likelihoods prevents the scalability of existing methods. The proposed method addresses this challenge by dividing responses into subvectors to be analyzed separately and in parallel on a distributed platform using pairwise composite likelihood. Theoretical challenges related to combining results from dependent data are overcom

SUBMITTER: Hector EC 

PROVIDER: S-EPMC8218880 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature