Application of fused graphical lasso to statistical inference for multiple sparse precision matrices.
PLoS One
; 19(5): e0304264, 2024.
Article
de En
| MEDLINE
| ID: mdl-38820407
ABSTRACT
In this paper, the fused graphical lasso (FGL) method is used to estimate multiple precision matrices from multiple populations simultaneously. The lasso penalty in the FGL model is a restraint on sparsity of precision matrices, and a moderate penalty on the two precision matrices from distinct groups restrains the similar structure across multiple groups. In high-dimensional settings, an oracle inequality is provided for FGL estimators, which is necessary to establish the central limit law. We not only focus on point estimation of a precision matrix, but also work on hypothesis testing for a linear combination of the entries of multiple precision matrices. We apply a de-biasing technology, which is used to obtain a new consistent estimator with known distribution for implementing the statistical inference, and extend the statistical inference problem to multiple populations. The corresponding de-biasing FGL estimator and its asymptotic theory are provided. A simulation study and an application of the diffuse large B-cell lymphoma data show that the proposed test works well in high-dimensional situation.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Algorithmes
Limites:
Humans
Langue:
En
Journal:
PLoS ONE (Online)
/
PLoS One
/
PLos ONE
Sujet du journal:
CIENCIA
/
MEDICINA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
États-Unis d'Amérique