Unbalanced distributed estimation and inference for precision matrices

Ensiyeh Nezakati and Eugen Pircalabelu
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Ensiyeh Nezakati: Université catholique de Louvain, LIDAM/ISBA, Belgium
Eugen Pircalabelu: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2021031, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

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.

Keywords: Gaussian graphical models; Precision matrix; Lasso penalization; Unbalanced distributed setting; De-biased estimator; Confidence distribution (search for similar items in EconPapers)
Pages: 20
Date: 2021-01-01
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvad:2021031

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