Novel model selection criteria on sparse biological networks

Date

2019-09-01

Author

Bulbul, G. B.
Purutçuoğlu Gazi, Vilda
Purutcuoglu, E.

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In statistical literature, gene networks are represented by graphical models, known by their sparsity in high dimensions. In this study, we suggest novel model selection criteria, namely, ICOMP, CAIC and CAICF to apply on simulated gene networks when selecting an optimal model among alternative estimated networks' constructions. In this description, we build models with the Gaussian graphical model (GGM) and the inference of GGM is achieved via the graphical lasso method. In the assessment of our proposed model selection criteria, we compare their accuracies with other well-known criteria in this field under various dimensions and topologies of networks.

Subject Keywords

Environmental Engineering, General Agricultural and Biological Sciences, Environmental Chemistry

URI

https://hdl.handle.net/11511/47144

Journal

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY

DOI

https://doi.org/10.1007/s13762-019-02206-9

Collections

Department of Statistics, Article

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Citation Formats

G. B. Bulbul, V. Purutçuoğlu Gazi, and E. Purutcuoglu, “Novel model selection criteria on sparse biological networks,” INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, pp. 5359–5364, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47144.