Online Program Home
My Program

Abstract Details

Activity Number: 122 - Novel Statistical Methods in the Analysis of Big Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #307190
Title: Subsampled Information Criterion for Bayesian Model Selection in Big Data Setting
Author(s): Guanyu Hu* and Lijiang Geng and Yishu Xue
Companies: University of Connecticut and University of Connecticut and University of Connecticut
Keywords: DIC; IC; MCMC; Nonuniform Subsample

In big data time, less attention is paid to Bayesian methods as they are known to be computationally intensive for both parameter estimation and model selection, while existing literature focus more on approaches to speed up Markov chain Monte Carlo (MCMC). Deviance-based model selection criteria like the deviance information criterion (DIC) Bayesian predictive information criterion (BPIC) are well-known Bayesian criteria for model selection. In this article, we introduce the subsampled DIC and the subsampled information criterion ICAT introduced by Ando and Tsay (2010) in the big data context. Under reasonable regularity conditions, we show that our proposed subsampled criteria closely approximate their full data counterparts. Extensive simulation studies are conducted to evaluate the empirical performance of the proposed criteria. The usage of our proposed criteria is further illustrated with the analysis of two large datasets, the Public Use Microdata Sample (PUMS) data and the cover type data.

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

Back to the full JSM 2019 program