Title: Unbalanced distributed estimation and inference for covariate-adjusted Gaussian graphical models
Authors: Eugen Pircalabelu - Université catholique de Louvain (Belgium) [presenting]
Ensiyeh Nezakati Rezazadeh - Université catholique de Louvain (Belgium) (Belgium)
Abstract: A distributed estimation and statistical inference framework are introduced for the sparse precision matrix in the covariate-adjusted Gaussian graphical models under the unbalanced splitting setting. This type of splitting arises when the datasets from different sources cannot be aggregated on one single machine or when the available machines are of different powers. A de-biased estimator of the precision matrix on every single machine is proposed, and theoretical guarantees are provided. Moreover, a new de-biased estimator that is pooled across the machines using the confidence distribution is proposed. It is shown to enjoy consistency and asymptotic normality, and we provide statistical inference strategies based on it. The performance of this estimator is investigated via a simulation study and a real data example. It is shown that the performance of this estimator is close to the non-distributed estimator, which uses the entire dataset.