Mixed‐type multivariate response regression with covariance estimation

  title={Mixed‐type multivariate response regression with covariance estimation},
  author={Karl Oskar Ekvall and Aaron J. Molstad},
  journal={Statistics in Medicine},
  pages={2768 - 2785}
We propose a new method for multivariate response regression and covariance estimation when elements of the response vector are of mixed types, for example some continuous and some discrete. Our method is based on a model which assumes the observable mixed‐type response vector is connected to a latent multivariate normal response linear regression through a link function. We explore the properties of this model and show its parameters are identifiable under reasonable conditions. We impose no… 
1 Citations
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