7 papers · 1 filter
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…
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…
Fast and reliable confidence intervals for a variance component
Yiqiao Zhang, Karl Oskar Ekvall, Aaron J. Molstad
We show that confidence intervals in a variance component model, with asymptotically correct uniform coverage probability, can be obtained by inverting certain test-statistics base…
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…
Conditional probability tensor decompositions for multivariate categorical response regression
Aaron J. Molstad, Xin Zhang
In many modern regression applications, the response consists of multiple categorical random variables whose probability mass is a function of a common set of predictors. In this a…