Sentence examples for model with variances from inspiring English sources

The component studies are designed to estimate a common effect size δ (we assume the fixed effects model), with variances depending on the sample sizes in the individual studies.

Both estimators use auxiliary information (x) and can be obtained through a model-assisted reasoning where the ratio model with variance proportional to x plays an important role.

In this setup and without loss of generality, we consider only the Gaussian model with variance σ2 for the physical devices TY∣X and T Z ∣ X ̂. Figure 2 compares the receiver operating characteristic (ROC) curves associated with the two different strategies.

The sum score correlations are now lower than under the model with variance = 1.

We used a Generalised Least Square model with variance-covariance matrix given by Brownian motion on the tree to estimate the association coefficient and its significance.

The full model with variance components and fit statistics is given in supplemental Table A1 of the online appendix (available at http://dx.doi.org/10.2337/dc08-0912).

As the full pedigree of the population is known, this was accounted for using a mixed model with variance components to account for each stratum (between funnels and between outcrossed plants within funnels).

Because the full pedigree of the population is known, this was accounted for using a mixed model with variance components to account for each stratum (between funnels and between outcrossed plants within funnels).

Estimation and inference in classical linear mixed models are based on the marginal model, which for equation (1) is the bivariate normal model with variance-covariance δ i + Ω. Assume for the sake of simplicity that the within-study covariance matrix δ i = δ for all studies.

The covariance structure for β i, denoted by Σ i, is either an independence model with variance τ i or a spatial smoothing model with exponential covariance function parameterized by range φ i, partial sill σ i, and nugget τ i (Cressie 1993).

Figure  1E shows the attenuation effect when the true model is a one-parameter IRT model with variance 1, where all items are endorsed by fewer than half the participants (i.e., all β parameters larger than the average latent score).