Unbalanced distributed estimation and inference for precision matrices
(2021) , 20 pages
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- Authors
Nezakati Rezazadeh, Ensiyeh 
- Abstract
- This paper studies the estimation of Gaussian graphical models in the unbalanced distributed framework. Unbalanced distributing is an effective approach when the available machines are of different powers or when the existing dataset comes from different resources with different sizes and can not be aggregated in one single computer. In this paper, we propose a new aggregated estimator of the precision matrix and justify such an approach by both theoretical and practical arguments. The limit distribution and consistency of this estimator are investigated. Furthermore, a procedure for performing statistical inference is proposed. On the practical side, a simulation study and real data examples are illustrated. We show that the performance of the distributed estimator is similar to that of the non-distributed estimator using the full data.
- Affiliations
Citations
Nezakati Rezazadeh, E., & Pircalabelu, E. (2021). Unbalanced distributed estimation and inference for precision matrices (LIDAM Discussion Paper ISBA 2021/31). https://hdl.handle.net/2078.5/119177