154 citations · 196 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
An explicit mean-covariance parameterization for multivariate response linear regression
Aaron J. Molstad, Guangwei Weng, Charles R. Doss +1
We develop a new method to fit the multivariate response linear regression model that exploits a parametric link between the regression coefficient matrix and the error covariance…
Automatic Response Category Combination in Multinomial Logistic Regression
Bradley S. Price, Charles J. Geyer, Adam J. Rothman
We propose a penalized likelihood method that simultaneously fits the multinomial logistic regression model and combines subsets of the response categories. The penalty is non diff…
Shrinking characteristics of precision matrix estimators
Aaron J. Molstad, Adam J. Rothman
We propose a framework to shrink a user-specified characteristic of a precision matrix estimator that is needed to fit a predictive model. Estimators in our framework minimize the…
Indirect multivariate response linear regression
Aaron J. Molstad, Adam J. Rothman
We propose a new class of estimators of the multivariate response linear regression coefficient matrix that exploits the assumption that the response and predictors have a joint mu…
A new approach to Cholesky-based covariance regularization in high dimensions
Adam J. Rothman, Elizaveta Levina, Ji Zhu
In this paper we propose a new regression interpretation of the Cholesky factor of the covariance matrix, as opposed to the well known regression interpretation of the Cholesky fac…