Bayesian inference for a covariance matrix

@article{Alvarez2014BayesianIF,
  title={Bayesian inference for a covariance matrix},
  author={Ignacio Alvarez and Jarad Niemi and M. Simpson},
  journal={arXiv: Methodology},
  year={2014}
}
  • Ignacio Alvarez, Jarad Niemi, M. Simpson
  • Published 2014
  • Mathematics
  • arXiv: Methodology
  • Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly modeled, e.g. random-intercept, random-slope models. A Bayesian analysis of these problems requires a prior on the covariance matrix. Here we assess, through a simulation study and a real data set, the impact this prior choice has on posterior inference of the covariance matrix. Inverse Wishart distribution is the natural choice for a… CONTINUE READING
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