Graphical models via joint quantile regression with component selection

Hyonho Chun, Myung Hee Lee, James C. Fleet and Ji Hwan Oh

Journal of Multivariate Analysis, 2016, vol. 152, issue C, 162-171

Abstract: A graphical model is used for describing interrelationships among multiple variables. In many cases, the multivariate Gaussian assumption is made partly for its simplicity but the assumption is hardly met in actual applications. In order to avoid dependence on a rather strong assumption, we propose to infer the graphical model via joint quantile regression with component selection, since the components of quantile regression carry information to infer the conditional independence. We demonstrate the advantages of our approach using simulation studies and apply our method to an interesting real biological dataset, where the dependence structure is highly complex.

Keywords: Gaussian graphical models; Conditional quantile; Component selection; Generalized additive models (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.jmva.2016.07.012

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