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Among these abovementioned methods, Andersen et al. [15] incorporated radio frequency inhomogeneity correction into probabilistic classification model to partition an MR image.
We propose using a flexible hierarchical Bayesian model to partition the variability and quantify uncertainties in a unified framework.
The proposed approach first builds a Bayesian model on both continuous data and discrete data, and then extends the model to partition multiple-phenotype data.
Kang et al. and Christian have outlined some of the assumptions that are implicit in using the classical twin model to partition variance into genetic and environmental components.
A common way of specifying multilevel models is to build them in a sequential manner [ 13], starting with a non-conditional (empty) model to partition variance across levels and assess its statistical significance [ 25].
In the first step, we used an unconditional means model to partition and quantify the total variability in unmet needs into its within- and between-persons components, without regard to time.
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We use the animal model framework to partition variation in the observed traits and the latent factor traits into additive genetic variation and residuals.
We used reduced models to partition the variance for each sex separately.
We used mixed models to partition odor variance within and between people and between neighborhoods, and to quantify relationships between environmental factors and odor.
In fact, because we will use multivariate regression models to partition the overall variance, we expect to have more power than estimated.
Classical breeding studies utilize reciprocal crosses with linear modeling to partition heritability between the nuclear and cytoplasmic genomes (Singh, 1965; Crane and Nyquist, 1967; Eenink and Garretsen, 1980; Miura et al., 1997; Primomo et al., 2002).
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