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Publications [#257846] of David B. Dunson


Papers Published

  1. Kundu, S; Dunson, DB, Bayes variable selection in semiparametric linear models., Journal of the American Statistical Association, vol. 109 no. 505 (March, 2014), pp. 437-447, ISSN 0162-1459 [doi]
    (last updated on 2017/12/11)

    There is a rich literature on Bayesian variable selection for parametric models. Our focus is on generalizing methods and asymptotic theory established for mixtures of g-priors to semiparametric linear regression models having unknown residual densities. Using a Dirichlet process location mixture for the residual density, we propose a semiparametric g-prior which incorporates an unknown matrix of cluster allocation indicators. For this class of priors, posterior computation can proceed via a straightforward stochastic search variable selection algorithm. In addition, Bayes factor and variable selection consistency is shown to result under a class of proper priors on g even when the number of candidate predictors p is allowed to increase much faster than sample size n, while making sparsity assumptions on the true model size.
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