154 citations · 196 across the 15 of their papers we have counts for
15 papers
Nonparametric estimation of time-varying network connections by multi-stage smoothing
Jeonghwan Lee, Tianxi Li, Adam J. Rothman
We consider the problem of estimating the underlying edge probabilities of a time-varying network observed at multiple time points. The probability structure is represented by a ti…
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…
Sparse Multivariate Linear Regression with Strongly Associated Response Variables
Daeyoung Ham, Bradley S. Price, Adam J. Rothman
We propose new methods for multivariate linear regression when the regression coefficient matrix is sparse and the error covariance matrix is dense. We assume that the error covari…
A Bayesian Generalized Bridge Regression Approach to Covariance Estimation in the Presence of Covariates
Christina Zhao, Ding Xiang, Galin L. Jones +1
A hierarchical Bayesian approach that permits simultaneous inference for the regression coefficient matrix and the error precision (inverse covariance) matrix in the multivariate l…
Fitted value shrinkage
Daeyoung Ham, Adam J. Rothman
We propose a penalized least-squares method to fit the linear regression model with fitted values that are invariant to invertible linear transformations of the design matrix. This…
A likelihood-based approach for multivariate categorical response regression in high dimensions
Aaron J. Molstad, Adam J. Rothman
We propose a penalized likelihood method to fit the bivariate categorical response regression model. Our method allows practitioners to estimate which predictors are irrelevant, wh…