Online Variational Bayes Inference for High-Dimensional Correlated Data

Published

Journal Article

© 2016 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. High-dimensional data with hundreds of thousands of observations are becoming commonplace in many disciplines. The analysis of such data poses many computational challenges, especially when the observations are correlated over time and/or across space. In this article, we propose flexible hierarchical regression models for analyzing such data that accommodate serial and/or spatial correlation. We address the computational challenges involved in fitting these models by adopting an approximate inference framework. We develop an online variational Bayes algorithm that works by incrementally reading the data into memory one portion at a time. The performance of the method is assessed through simulation studies. The methodology is applied to analyze signal intensity in MRI images of subjects with knee osteoarthritis, using data from the Osteoarthritis Initiative. Supplementary materials for this article are available online.

Full Text

Duke Authors

Cited Authors

  • Kabisa, S; Dunson, DB; Morris, JS

Published Date

  • April 2, 2016

Published In

Volume / Issue

  • 25 / 2

Start / End Page

  • 426 - 444

Electronic International Standard Serial Number (EISSN)

  • 1537-2715

International Standard Serial Number (ISSN)

  • 1061-8600

Digital Object Identifier (DOI)

  • 10.1080/10618600.2014.998336

Citation Source

  • Scopus