Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#329353] of David B. Dunson


Papers Published

  1. Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian tensor regression, Journal of Machine Learning Research, vol. 18 (August, 2017), pp. 1-31
    (last updated on 2019/05/23)

    ©2017 Rajarshi Guhaniyogi and Shaan Qamar and David B. Dunson. We propose a Bayesian approach to regression with a scalar response on vector and tensor covariates. Vectorization of the tensor prior to analysis fails to exploit the structure, often leading to poor estimation and predictive performance. We introduce a novel class of multiway shrinkage priors for tensor coefficients in the regression setting and present posterior consistency results under mild conditions. A computationally efficient Markov chain Monte Carlo algorithm is developed for posterior computation. Simulation studies illustrate substantial gains over existing tensor regression methods in terms of estimation and parameter inference. Our approach is further illustrated in a neuroimaging application.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320