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The assumptions - and what goes wrong

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$\displaystyle \mathbf{y}\sim n \left (\mathbf{X}\boldsymbol{\beta},\sigma^2\boldsymbol{I}\right )

may be wrong.

Simple variance exceptions are easy to handle:

  • $ Vy_i=u_i \sigma^2$ where $ u_i$ are known
  • $ \Sigma_{\mathbf{y}}=\sigma^2 B$ where $ B$ is known
  • $ \Sigma_{\mathbf{y}}$ may contain ``a few'' unknown parameters

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