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Found 7 papers, 5 papers with code
Robust Inference for High-Dimensional Linear Models via Residual Randomization
We propose a residual randomization procedure designed for robust Lasso-based inference in the high-dimensional setting.
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
We propose a neighborhood selection approach to estimate the structure of Gaussian functional graphical models, where we first estimate the neighborhood of each node via a function-on-function regression and subsequently recover the entire graph structure by combining the estimated neighborhoods.
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
We first define a functional differential graph that captures the differences between two functional graphical models and formally characterize when the functional differential graph is well defined.
Direct Estimation of Differential Functional Graphical Models
We consider the problem of estimating the difference between two functional undirected graphical models with shared structures.
On Causal Discovery with Equal Variance Assumption
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variances.
Methodology Computation
On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example
Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine learning.
A Variational EM Method for Mixed Membership Models with Multivariate Rank Data: an Analysis of Public Policy Preferences
In this article, we consider modeling ranked responses from a heterogeneous population.
Methodology Applications
