Zobrazeno 1 - 10
of 1 240
pro vyhledávání: '"Dunson, David B"'
Feature selection can greatly improve performance and interpretability in machine learning problems. However, existing nonparametric feature selection methods either lack theoretical error control or fail to accurately control errors in practice. Man
Externí odkaz:
http://arxiv.org/abs/2410.02208
Autor:
Mauri, Lorenzo, Dunson, David B.
This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors in the linear predictor. Bayesian approaches are useful for character
Externí odkaz:
http://arxiv.org/abs/2409.17441
Ecological and conservation studies monitoring bird communities typically rely on species classification based on bird vocalizations. Historically, this has been based on expert volunteers going into the field and making lists of the bird species tha
Externí odkaz:
http://arxiv.org/abs/2406.15844
It is increasingly common in a wide variety of applied settings to collect data of multiple different types on the same set of samples. Our particular focus in this article is on studying relationships between such multiview features and responses. A
Externí odkaz:
http://arxiv.org/abs/2406.00778
Our interest is in replicated network data with multiple networks observed across the same set of nodes. Examples include brain connection networks, in which nodes corresponds to brain regions and replicates to different individuals, and ecological n
Externí odkaz:
http://arxiv.org/abs/2405.20936
Tree graphs are routinely used in statistics. When estimating a Bayesian model with a tree component, sampling the posterior remains a core difficulty. Existing Markov chain Monte Carlo methods tend to rely on local moves, often leading to poor mixin
Externí odkaz:
http://arxiv.org/abs/2405.03096
Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum of distinct subtypes, motivating several studies to identify clusters
Externí odkaz:
http://arxiv.org/abs/2405.01746
Bayesian factor analysis is routinely used for dimensionality reduction in modeling of high-dimensional covariance matrices. Factor analytic decompositions express the covariance as a sum of a low rank and diagonal matrix. In practice, Gibbs sampling
Externí odkaz:
http://arxiv.org/abs/2404.03805
Broadly, the goal when clustering data is to separate observations into meaningful subgroups. The rich variety of methods for clustering reflects the fact that the relevant notion of meaningful clusters varies across applications. The classical Bayes
Externí odkaz:
http://arxiv.org/abs/2403.04912
Autor:
Stolf, Federica, Dunson, David B.
Joint species distribution models are popular in ecology for modeling covariate effects on species occurrence, while characterizing cross-species dependence. Data consist of multivariate binary indicators of the occurrences of different species in ea
Externí odkaz:
http://arxiv.org/abs/2402.13384