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Bayesian analysis of skew-t multivariate null intercept measurement error model

Abstract

The multivariate skew-t distribution (J Multivar Anal 79:93–113, 2001; J R Stat Soc, Ser B 65:367–389, 2003; Statistics 37:359–363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew–normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763–771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.

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Correspondence to Reiko Aoki.

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Lachos, V.H., Cancho, V.G. & Aoki, R. Bayesian analysis of skew-t multivariate null intercept measurement error model. Stat Papers 51, 531–545 (2010). https://doi.org/10.1007/s00362-008-0138-z

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Keywords

  • Skew-t distribution
  • Gibbs algorithm
  • Metropolis-Hasting
  • Skewness
  • Multivariate null intercepts model
  • Measurement error