JSTOR Daily

Accounting for Mutation Effects in the Additive Genetic Variance-Covariance Matrix and Its Inverse

Procedures for calculating the additive genetic variance-covariance matrix and its inverse are adapted to accommodate the occurrence of mutations in the genome. The inverse matrix can be used in mixed ...
Simon Fraser University

Example 24.9: Optimal Design in the Presence of Covariance

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 ...
JSTOR Daily

Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm

The expectation maximization (EM) algorithm is a popular, and often remarkably simple, method for maximum likelihood estimation in incomplete-data problems. One criticism of EM in practice is that ...
Simon Fraser University

Covariance Matrix

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 ...