Partially Linear Additive Gaussian Graphical Models
Machine Learning
2019-06-11 v1 Machine Learning
Abstract
We propose a partially linear additive Gaussian graphical model (PLA-GGM) for the estimation of associations between random variables distorted by observed confounders. Model parameters are estimated using an -regularized maximal pseudo-profile likelihood estimator (MaPPLE) for which we prove -sparsistency. Importantly, our approach avoids parametric constraints on the effects of confounders on the estimated graphical model structure. Empirically, the PLA-GGM is applied to both synthetic and real-world datasets, demonstrating superior performance compared to competing methods.
Cite
@article{arxiv.1906.03362,
title = {Partially Linear Additive Gaussian Graphical Models},
author = {Sinong Geng and Minhao Yan and Mladen Kolar and Oluwasanmi Koyejo},
journal= {arXiv preprint arXiv:1906.03362},
year = {2019}
}