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By using the latent variables, we design time-frequency masks which reduce the nontarget sources and enhance the target source.
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Using the latent variable modeling approach with PLDA, H0 and H1 correspond to the models as shown in Figure 3.
Finally, we derived the SEM equations and implemented these in the statistical software R using the latent variable analysis (lavaan) package.
Using the latent variable formulation, we can define a latent variable Z i * that is related to Z i via the following equation: Z i = 1 if Z i * > 0, 0 otherwise.
ICC was calculated using the latent variable method.
When the external variables are unmeasured or imprecise, we are interested in using the latent variable basis, L, to discover genes of interest (Fig. 1).
The variance partition coefficient for each level of the model (i.e., fledgling, clutch, and breeding pair) was estimated using the latent variable technique [ 13].
Variance partition coefficients (VPCs) were estimated from the variance components of the final model including both fixed and random effects using the latent variable technique [ 15].
Multi-level models yielded similar ORs and CIs as GEE models, and low intraclass correlations (PTB 0.0048, SGA birth 0.0032 – calculated using the latent variable method [ 41]).
Because the results of statin treatments on arthritis induction using the latent variable model were similar to the results using the log-likelihood model, we have chosen to show the results of the latter model.
The VPC at each level was calculated using the latent variable method, which assumes a threshold model, and approximating the level-1 (individual) variance by π/3 (≈3.29) (27, 29).
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