Search Results - "Dunson, David"

Dirichlet–Laplace Priors for Optimal Shrinkage by Bhattacharya, Anirban, Pati, Debdeep, Pillai, Natesh S., Dunson, David B.

“…Penalized regression methods, such as L ₁ regularization, are routinely used in high-dimensional applications, and there is a rich literature on optimality…”
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Robust Bayesian Inference via Coarsening by Miller, Jeffrey W., Dunson, David B.

“…The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a…”
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Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods by Nishimura, Akihiko, Dunson, David B, Lu, Jianfeng

“…Summary Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article we present an extension that can efficiently explore…”
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GENERALIZED DOUBLE PARETO SHRINKAGE by Armagan, Artin, Dunson, David B., Lee, Jaeyong

“…We propose a generalized double Pareto prior for Bayesian shrinkage estimation and inferences in linear models. The prior can be obtained via a scale mixture…”
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Bayesian cumulative shrinkage for infinite factorizations by Legramanti, Sirio, Durante, Daniele, Dunson, David B

“…Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the…”
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Bayesian consensus clustering by Lock, Eric F, Dunson, David B

“…In biomedical research a growing number of platforms and technologies are used to measure diverse but related information, and the task of clustering a set of…”
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Nonparametric Bayes Modeling of Populations of Networks by Durante, Daniele, Dunson, David B., Vogelstein, Joshua T.

“…Replicated network data are increasingly available in many research fields. For example, in connectomic applications, interconnections among brain regions are…”
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BAYESIAN MANIFOLD REGRESSION by Yang, Yun, Dunson, David B.

“…There is increasing interest in the problem of nonparametric regression with high-dimensional predictors. When the number of predictors D is large, one…”
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Bayesian Factor Analysis for Inference on Interactions by Ferrari, Federico, Dunson, David B.

“…This article is motivated by the problem of inference on interactions among chemical exposures impacting human health outcomes. Chemicals often co-occur in the…”
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Bayesian Pyramids: identifiable multilayer discrete latent structure models for discrete data by Gu, Yuqi, Dunson, David B

“…Abstract High-dimensional categorical data are routinely collected in biomedical and social sciences. It is of great importance to build interpretable…”
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Ellipsoid fitting with the Cayley transform by Melikechi, Omar, Dunson, David B.

“…We introduce Cayley transform ellipsoid fitting (CTEF), an algorithm that uses the Cayley transform to fit ellipsoids to noisy data in any dimension. Unlike…”
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MCMC for Imbalanced Categorical Data by Johndrow, James E., Smith, Aaron, Pillai, Natesh, Dunson, David B.

“…Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by…”
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Efficient manifold approximation with spherelets by Li, Didong, Mukhopadhyay, Minerva, Dunson, David B.

“…In statistical dimensionality reduction, it is common to rely on the assumption that high dimensional data tend to concentrate near a lower dimensional…”
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Bayesian Compressed Regression by Guhaniyogi, Rajarshi, Dunson, David B.

“…As an alternative to variable selection or shrinkage in high-dimensional regression, we propose to randomly compress the predictors prior to analysis. This…”
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Outlier detection for multi-network data by Dey, Pritam, Zhang, Zhengwu, Dunson, David B

“…Abstract Motivation It has become routine in neuroscience studies to measure brain networks for different individuals using neuroimaging. These networks are…”
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Targeted Random Projection for Prediction From High-Dimensional Features by Mukhopadhyay, Minerva, Dunson, David B.

“…We consider the problem of computationally efficient prediction with high dimensional and highly correlated predictors when accurate variable selection is…”
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Bayesian Factorizations of Big Sparse Tensors by Zhou, Jing, Bhattacharya, Anirban, Herring, Amy H., Dunson, David B.

“…It has become routine to collect data that are structured as multiway arrays (tensors). There is an enormous literature on low rank and sparse matrix…”
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PPA: Principal parcellation analysis for brain connectomes and multiple traits by Liu, Rongjie, Li, Meng, Dunson, David B.

“…•We propose a new approach called principal parcellation analysis (PPA) for predicting human traits using the brain connectome.•PPA uses tractography-based…”
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How to make more out of community data? A conceptual framework and its implementation as models and software by Ovaskainen, Otso, Tikhonov, Gleb, Norberg, Anna, Guillaume Blanchet, F., Duan, Leo, Dunson, David, Roslin, Tomas, Abrego, Nerea, Chave, Jerome

“…Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate…”
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Modeling Recurrent Failures on Large Directed Networks by Zhai, Qingqing, Ye, Zhisheng, Li, Cheng, Revie, Matthew, Dunson, David B.

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