where VAR 1 and VAR 2 are variance components from the first and second sampling phases, respectively; S zz represents the variance covariance matrix of vector Z T ; and S bb represents the variance covariance matrix of vector b.
where μ 0,Y, μ 1,Y, Cov 0,Y, and Cov 1,Y are in-control and out-of-control mean vector and covariance matrix of vector Y, respectively.
where μ 0,Y and Cov 0,Y are the in-control mean vector and covariance matrix, and μ 1,Y and Cov 1,Y are the out-of-control mean-vector and covariance matrix of vector Y, respectively.
A false-color matrix of vector correlations displayed affinities among species consistent with higher-order taxonomy.
Let ∑ε be the I × I variance covariance matrix of vector ε̅, which is defined in eq 5.
Combining equations (2a) and (2b), we can obtain the following covariance matrix of vector Z: (3) Σ = Var Z, Z = σ 2 Φ.
where is a matrix of weight vectors with columns, is a vector of class biases, and is a probability normalization term.
By subtracting the mean from both the observed and latent variables it is possible to define the covariance matrix of a vector of variables in terms of expectations of vector products.
In estimating variance, both mean vector and covariance matrix are required [29]; hence, lower bound on SINR is chosen because covariance matrix of random vector is known.
One common example is the rank of the matrix of data vectors, and has been exploited in the past to some extent.
