8 papers
Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning
Minerva Mukhopadhyay, Jacie R. McHaney, Bharath Chandrasekaran +1
Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mi…
Targeted Random Projection for Prediction from High-Dimensional Features
Minerva Mukhopadhyay, David B. Dunson
We consider the problem of computationally-efficient prediction with high dimensional and highly correlated predictors when accurate variable selection is effectively impossible. D…
Estimating densities with nonlinear support using Fisher-Gaussian kernels
Minerva Mukhopadhyay, Didong Li, David B Dunson
Current tools for multivariate density estimation struggle when the density is concentrated near a nonlinear subspace or manifold. Most approaches require choice of a kernel, with…
Bayes Factor Asymptotics for Variable Selection in the Gaussian Process Framework
Minerva Mukhopadhyay, Sourabh Bhattacharya
Although variable selection is one of the most popular areas of modern statistical research, much of its development has taken place in the classical paradigm compared to the Bayes…
Targeted Random Projection for Prediction from High-Dimensional Features
Minerva Mukhopadhyay, David B. Dunson
We consider the problem of computationally-efficient prediction from high dimensional and highly correlated predictors in challenging settings where accurate variable selection is…
Efficient Manifold and Subspace Approximations with Spherelets
Didong Li, Minerva Mukhopadhyay, David B. Dunson
In statistical dimensionality reduction, it is common to rely on the assumption that high dimensional data tend to concentrate near a lower dimensional manifold. There is a rich li…