Theory of matrix variate distributions extends classical univariate and multivariate approaches to random matrices, accommodating dependence structures across both rows and columns. Fundamental ...
The BLOCKS statement finds a design that maximizes the determinant |X'AX| of the treatment information matrix, where A depends on the block or covariate model. Alternatively, you can directly specify ...
The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...
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