Online Variational Bayes Inference for High-Dimensional Correlated Data. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Kabisa, Sylvie (Tchumtchoua); Dunson, David B.; Morris, Jeffrey S.
• Is Part Of: Journal of computational and graphical statistics. Volume 25:Issue 2(2016)
• Page Start: 426
• Page End: 444
• Publication Date: 2016-04-02
• Subjects: Conditional autoregressive model -- Correlated high-dimensional data -- Hierarchical model -- Image data -- Nonparametric Bayes -- Online variational Bayes; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2014.998336
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract : 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.