In this section
@article{Filippi:2016:10.1214/16-EJS1171,
author = {Filippi, S and Holmes, CC and Nieto-Barajas, LE},
doi = {10.1214/16-EJS1171},
journal = {Electronic Journal of Statistics},
pages = {3338--3354},
title = {Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures},
url = {http://dx.doi.org/10.1214/16-EJS1171},
volume = {10},
year = {2016}
}
TY - JOUR
AB - In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a “null model” of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.
AU - Filippi,S
AU - Holmes,CC
AU - Nieto-Barajas,LE
DO - 10.1214/16-EJS1171
EP - 3354
PY - 2016///
SN - 1935-7524
SP - 3338
TI - Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures
T2 - Electronic Journal of Statistics
UR - http://dx.doi.org/10.1214/16-EJS1171
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000390364400053&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/59678
VL - 10
ER -