title: Measures of biomarker dependence using a copula-based multivariate epsilon-skew-normal family of distributions.
abstract: In this note we develop a new multivariate copula model based on epsilon-skew-normal marginal densities for the purpose of examining biomarker dependency structures. We illustrate the flexibility and utility of this model via a variety of graphical tools and a data analysis example pertaining to salivary biomarker. The multivariate normal model is a sub-model of the multivariate epsilon-skew-normal distribution.
authors.affiliation: Department of Biostatistics, University at Buffalo, 706 Kimball Tower, 3435 Main St., Buffalo, NY 14214-3000, USA.
authors.forename: Alan D
authors.initials: AD
authors.lastname: Hutson
authors.affiliation: Department of Biostatistics, University at Buffalo, 706 Kimball Tower, 3435 Main St., Buffalo, NY 14214-3000, USA.
authors.forename: Gregory E
authors.initials: GE
authors.lastname: Wilding
authors.affiliation: Department of Biostatistics, University at Buffalo, 706 Kimball Tower, 3435 Main St., Buffalo, NY 14214-3000, USA.
authors.forename: Terry L
authors.initials: TL
authors.lastname: Mashtare
authors.affiliation: Department of Biostatistics, University at Buffalo, 706 Kimball Tower, 3435 Main St., Buffalo, NY 14214-3000, USA.
authors.forename: Albert
authors.initials: A
authors.lastname: Vexler
keywords: association; conditional correlation; conditional dependence; conditional expectation