Abstract

Gaussian graphical models (GGMs) have been investigated to infer

dependence (or network) structure among high-dimensional data by estimating

a precision matrix.

However, while many estimation methods for GGM have

been developed, methods for testing the equality of two precision matrixes are

still limited.

Because testing the equality of the precision matrix depends on

other given precision matrices, we develop a weighted conditional network testing for considering other given precision matrices information and also provide

theoretical properties. None of the existing methods can be applied to test conditional differences when other networks are conditionally given and different. We

demonstrate the advantage of our approach using a simulation study and genetic

pathway analysis.

Information

Preprint No.SS-2024-0330
Manuscript IDSS-2024-0330
Complete AuthorsTakwon Kim, Inyoung Kim, Ki-Ahm Lee
Corresponding AuthorsInyoung Kim
Emailsinyoungk@vt.edu

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Acknowledgments

Ki-Ahm Lee was supported by the Ministry of Education of the Republic of

Korea and the National Research Foundation of Korea (RS-2025-00515707).

Takwon Kim was supported by the National Research Foundation of Korea

(RS-2024-00351151). We are also grateful to the reviewers for their valuable

Supplementary Materials

Technical proofs, additional tables and figures referenced are available in a

separate file for the online Supplementary material of this paper.

Supplementary materials are available for download.