Sentence examples for estimates when applied from inspiring English sources

With randomly ascertained families, with or without population stratification, the prospective approach (modeling trait value given genotype) is found to be generally most effective, although the retrospective approach has some advantages with regard to estimation and interpretability of parameter estimates when applied to selected samples.

Being based on a Vandermonde decomposition of the prediction matrix, the matrix pencil method produces biased estimates when applied directly to data of the constant-Q model.

It is well known that subspace identification methods that assume open loop data without correlations between the input and noise, may give biased estimates when applied to closed loop data.

We found that the two methods yield indistinguishable estimates when applied to taxa whose limb proportions are similar to those of humans, but significantly different results when applied to taxa whose proportions differ from those of humans.

Thus, it appears that our finding that the DDM and CCNM yield significantly different estimates when applied to fossil hominins with body proportions that differ from those of humans is not peculiar to the DDM.

The third and fourth analyses show that the DDM and CCNM yield significantly different estimates when applied to fossil hominins with human-like body proportions and fossil hominins whose body proportions differ markedly from those of humans.

A necessity for yielding unbiased estimates when applying propensity score matching is a thoroughly considered model based on which the propensity scores are estimated (see Shadish and Steiner [2010]; Bryson et al. [2002]; Smith and Todd [2005] f.

Moreover, it does not lead to improved parameter estimates when applying Aeff, which was the actual aim of introducing their modified model.

This paper considers the properties of a bispectrum estimate when applied to a system with weak quadratic nonlinearity excited by the superposition of two sinusoids in the presence of additive Gaussian noise.

Similarly, in our study, using the ML-TIRM model, the total width of the 95% confidence interval surrounding the estimate was 38% of the estimate when applied to the entire four years of data and 61% when using the 2007 data only.

There is a real danger that a large classification will be over-specified (impairing incentives) and inaccurately estimated when applied to a smaller treatment population in a public-health system.