The random regression representation of MACE facilitates exploitation of principal component or factor analytic approaches for variance component estimation and prediction of breeding values for international sire evaluations.
To date, the researches that determine the anthraquinone glycoside content in rhubarb mainly employ a multi-component analytic method to ensure clinical medication safety [19 21].
We investigated how nine trained raters used a popular five-component analytic rubric by Jacobs et al. (1981; reproduced in Weigle, 2002).
Furthermore, we show the orthonormal harmonic waves satisfy the analytic condition and have positive instantaneous frequencies which are tightly associated with the average frequencies of the two mono-component analytic signals at each time.
The co-investigators will be involved more specifically in different components (Analytic themes) of the project.
Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE).
Principal component (PC) and factor analytic (FA) models provide a highly parsimonious structure for the VCV matrix compared to the standard multi-trait model, e.g. [ 6, 7] and they have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluations (MACE) [ 8].
Using latent variables has some effect on the modelled (co variance structures; because the latent variable model uses less parameters than the full (co variance matrix, the (co variance structure is somewhat restricted, similar to using only the main principal components in a principal component analysis or frequentist factor analytic model, e.g., [ 34].
We chose principal component analysis as almost all the factor analytic studies of manic symptoms had used this method.
Thus, parameter reduction using factor analytic, direct and bottom-up PC models worked well for variance component estimation for MACE.
