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20142024
most citedBayesian variable selection with shrinking and diffusing priors

246 citations · 277 across the 8 of their papers we have counts for

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6 papers · 1 filter

stat.ME2024

Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates

Abhishek Ojha, Naveen N. Narisetty

We address the challenge of conducting inference for a categorical treatment effect related to a binary outcome variable while taking into account high-dimensional baseline covaria…

stat.ME2021

Identifying regions of inhomogeneities in spatial processes via an M-RA and mixture priors

Marco H. Benedetti, Veronica J. Berrocal, Naveen N. Narisetty

Soils have been heralded as a hidden resource that can be leveraged to mitigate and address some of the major global environmental challenges. Specifically, the organic carbon stor…

stat.ME2020★ 7 cited

A Hierarchical Integrative Group LASSO (HiGLASSO) Framework for Analyzing Environmental Mixtures

Jonathan Boss, Alexander Rix, Yin-Hsiu Chen +6

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose…

stat.ME2019★ 4 cited

Evaluating proxy influence in assimilated paleoclimate reconstructions -- Testing the exchangeability of two ensembles of spatial processes

Trevor Harris, Bo Li, Nathan Steiger +3

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data…

stat.ME2019★ 19 cited

New methods for multiple testing in permutation inference for the general linear model

Tomas Mrkvicka, Mari Myllymaki, Mikko Kuronen +1

Permutation methods are commonly used to test significance of regressors of interest in general linear models (GLMs) for functional (image) data sets, in particular for neuroimagin…

stat.ME2015

Extremal Depth for Functional Data and Applications

Naveen N. Narisetty, Vijayan N. Nair

We propose a new notion called `extremal depth' (ED) for functional data, discuss its properties, and compare its performance with existing concepts. The proposed notion is based o…