Sentence examples for mean models see from inspiring English sources

For a description of methods based on structural nested mean models see Brumback et al.[ 23].

For a guide to analysts on how to choose among exponential conditional mean models see Manning and Mullahy (2001), Buntin and Zaslavsky (2004) and Manning et al. (2005).

The full model with also covariates in the covariance matrix was the best fitting model (Table  1; for estimates of the means model see Table  3).

An upper haze layer with a total thickness of unity is added to the mean cloud model (see Table 1) following table II of Crisp (1986).

The mechanical behavior of planar pantographic sheets has been studied by means of second gradient continuum models, see [1,2].

In addition to the summary percentage of risky choice, we derived an absolute measure for risk-preference in each state by fitting a mean-variance logistic regression model (see Appendix S1).

We aimed to probe by means of a mathematical model (see methods and additional files 1, 2 and 3, Figure 2) whether the relapsing-remitting dynamics in MS can emerge as a result of the intrinsic control properties of the immune system that allow it to oscillate (e.g. the Teff-Treg loop) [ 30].

By means of an animal model (see Methods section) applied to our extensive pedigree data, the variance in sex ratio among females that is not attributed to sampling error is decomposed into a maternally inherited part (σ m 2 ), which only incorporates this part of the sex ratio variation that is transmitted to their daughters, and a residual part (σ e 2 ) that estimates the remaining variation.

In order to include whole white matter summary measures of fractional anisotropy and mean diffusivity in the structural equation model (see below) we extracted the median fractional anisotropy and mean diffusivity for all non-zero white matter voxels included within the voxel-based analysis mask using the 'fslstats' function in FSL.

For instance, Purcell (2002) showed how environmental moderation on the means and variances can be modeled (see Fig.  2).

The ARMA-GARCH model models the mean equation via an (operatorname{ARMA}(p,q)) model, see Eq. (1), whereas the random noise components, represented by (epsilon_{t}), are modelled with a Generalized Autoregressive Conditional Heteroskedastic (GARCH), model.