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Measures of biomarker dependence using a copula-based multivariate epsilon-skew-normal family of distributions

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  • Alan D. Hutson
  • Gregory E. Wilding
  • Terry L. Mashtare
  • Albert Vexler

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.

Suggested Citation

  • Alan D. Hutson & Gregory E. Wilding & Terry L. Mashtare & Albert Vexler, 2015. "Measures of biomarker dependence using a copula-based multivariate epsilon-skew-normal family of distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2734-2753, December.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2734-2753
    DOI: 10.1080/02664763.2015.1049130
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    References listed on IDEAS

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    1. Branco, Márcia D. & Dey, Dipak K., 2001. "A General Class of Multivariate Skew-Elliptical Distributions," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 99-113, October.
    2. Bailey K. Fosdick & Adrian E. Raftery, 2012. "Estimating the Correlation in Bivariate Normal Data With Known Variances and Small Sample Sizes," The American Statistician, Taylor & Francis Journals, vol. 66(1), pages 34-41, February.
    3. M. C. Jones & M. J. Faddy, 2003. "A skew extension of the t‐distribution, with applications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 159-174, February.
    4. Alan Hutson, 2004. "Utilizing the Flexibility of the Epsilon-Skew-Normal Distribution for Common Regression Problems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(6), pages 673-683.
    5. Gregory Wilding & Xueya Cai & Alan Hutson & Zhangsheng Yu, 2011. "A linear model-based test for the heterogeneity of conditional correlations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2355-2366.
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