10 citations · 30 across the 25 of their papers we have counts for
25 papers
Constrained convex clustering for interpretable spatial domain detection in spot-based spatial transcriptomics
Hyun Jung Koo, Aaron J. Molstad
Popular technologies for generating spatially resolved transcriptomic data measure gene expression at the resolution of a "spot", i.e., a small tissue region 55 microns in diameter…
Universal inference for variance components
Yiqiao Zhang, Karl Oskar Ekvall, Aaron J. Molstad
We consider universal inference in variance components models, focusing on settings where the parameter is near or at the boundary of the parameter set. Two cases, which are not ha…
A direct approach to tree-guided feature aggregation for high-dimensional regression
Jinwen Fu, Aaron J. Molstad, Hui Zou
In high-dimensional linear models, sparsity is often exploited to reduce variability and achieve parsimony. Equi-sparsity, where one assumes that predictors can be aggregated into…
The Why and How of Convex Clustering
Eric C. Chi, Aaron J. Molstad, Zheming Gao +1
This survey reviews a clustering method based on solving a convex optimization problem. Despite the plethora of existing clustering methods, convex clustering has several uncommon…
Subspace decompositions for association structure learning in multivariate categorical response regression
Hongru Zhao, Aaron J. Molstad, Adam J. Rothman
Modeling the complex relationships between multiple categorical response variables as a function of predictors is a fundamental task in the analysis of categorical data. However, e…
Smooth and shape-constrained quantile distributed lag models
Yisen Jin, Aaron J. Molstad, Ander Wilson +1
Exposure to environmental pollutants during the gestational period can significantly impact infant health outcomes, such as birth weight and neurological development. Identifying c…