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Activity Number: 253 - Contributed Poster Presentations: Section on Statistical Computing
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #327219
Title: A Doubly Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis
Author(s): Emily Charlotte Hector* and Peter X.-K. Song
Companies: University of Michigan and University of Michigan
Keywords: Divide-and-conquer; Generalized method of moments; Parallel computing; Scalable computing

We present a divide-and-conquer procedure implemented in a distributed and parallelized MapReduce scheme for statistical estimation and inference of regression parameters with high-dimensional correlated responses with multi-level nested correlations. 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 subjects into independent groups and responses into subvectors to be analyzed separately and in parallel on a distributed platform. Theoretical challenges related to combining results from dependent data are overcome in a statistically efficient way using a meta-estimator derived from Hansen's Generalized Method of Moments. We provide a rigorous theoretical framework for efficient estimation, inference, and goodness-of-fit tests. We develop an R package for ease of implementation. We illustrate our method's performance with simulations and the analysis of a complex neuroimaging motivating dataset from an association study of the effects of iron deficiency on auditory recognition memory.

Authors who are presenting talks have a * after their name.

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