All structural equation models were computed with covariance-variance matrices and ML-estimation methods.
We chose unweighted least squares (ULS) estimation based on polychoric correlation matrices and variance-adjusted estimation methods to improve the precision of our estimates given these skewed categorical data [ 20, 22].
These two methods can offer closed-form solutions for coupling matrix and parameter estimation.
Therefore, whitening merely transforms the original source separation problem with mixing matrix A into a new problem with mixing matrix B A. The advantage is that B A is an orthogonal mixing matrix, and its estimation becomes easier[7].
The second method utilizes photoisomerization of provitamin D in nematic liquid crystalline (LC) matrix, and visual estimation of accumulated previtamin D becomes possible due to special design of a LC cell.
Through the use of an iterative approach, in which the pairwise-comparison matrix is populated by values drawn at random from a distribution defining each pairwise-comparison, it is likely that the frequency of inconsistency in the construction of the matrix and resulting estimation of factor weights was increased in this study.
For instance, a bivariate analysis in dairy cattle with 30 chromosomes would involve 30 genomic relationship matrices and the simultaneous estimation of 90 variance-covariance components.
For the blind 2D-DOA estimation algorithm that we have investigated, trilinear decomposition has been adopted for obtaining the estimated matrices, and then 2D-DOA estimation is correspondingly shown.
All factor analyses were conducted on polychoric correlation matrices and used maximum likelihood estimation.
Garcia-Cortes et al. [ 16] and Fouilloux and Laloë [ 17] have proposed sampling methods that, theoretically, allow the estimation of entire variance-covariance matrices, and, as a result, the estimation of the CD of contrasts among genetic levels of herds.
