Characterization of convolution splitting graphical models

Jean Peyhardi and Pierre Fernique

Statistics & Probability Letters, 2017, vol. 126, issue C, 59-64

Abstract: We aim at characterizing graphical models for convolution splitting distributions. Only marginal independence have been studied through the well-known Rao–Rubin condition. We generalize this condition for conditional independence and deduce the desired characterizations.

Keywords: Convolution splitting distribution; Rao–Rubin condition; Graphical model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spl.2017.02.018

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