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Publications [#329117] of David B. Dunson

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Papers Published

  1. Yang, Y; Dunson, DB, Bayesian Conditional Tensor Factorizations for High-Dimensional Classification, Journal of the American Statistical Association, vol. 111 no. 514 (April, 2016), pp. 656-669, Informa UK Limited [doi]
    (last updated on 2019/05/22)

    Abstract:
    © 2016, © American Statistical Association. In many application areas, data are collected on a categorical response and high-dimensional categorical predictors, with the goals being to build a parsimonious model for classification while doing inferences on the important predictors. In settings such as genomics, there can be complex interactions among the predictors. By using a carefully structured Tucker factorization, we define a model that can characterize any conditional probability, while facilitating variable selection and modeling of higher-order interactions. Following a Bayesian approach, we propose a Markov chain Monte Carlo algorithm for posterior computation accommodating uncertainty in the predictors to be included. Under near low-rank assumptions, the posterior distribution for the conditional probability is shown to achieve close to the parametric rate of contraction even in ultra high-dimensional settings. The methods are illustrated using simulation examples and biomedical applications. Supplementary materials for this article are available online.

 

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