Sentence examples for rank design from inspiring English sources

Wang et al. (1996) discuss that such models are identifiable for full rank design matrices.

The delayed code matrices and data forwarded from the relays shift the DSTC structure which results in the collapse of the rank design criterion and leads to degradation of decoding performance.

Hence, with full rank design matrices, the LSQ analysis seems the most suitable method when it can be assumed that the trait is controlled by one or two major genes and the heritabilities are 0.8 or greater.

The criteria of minimum G and G2 aberration are used to rank designs.

Furthermore, we showed that preexisting experimental data can be readily incorporated to help rank designs by how likely they are to be accurately reproduced experimentally.

In our study (Kadouri et al, 2001) on the modifying effect of the androgen receptor in BRCA carriers, we compared COX proportional hazards models to a variant of the log rank designed to overcome selection bias by comparison of outcome to expected penetrance according to the literature.

In our study (Kadouri et al, 2001) on the modifying effect of the androgen receptor-CAG-repeat length in BRCA carriers, we compared COX proportional hazard models to a variant of the log rank designed to overcome selection bias by comparison of outcome to expected penetrance according to the literature.

If the covariance matrix of the observed data vector is known, the optimal reduced-rank design is singular value decomposition (SVD -based [13, 14].

To solve the rank deficiencies in the undifferenced observation equations, the estimable parameters are formed to generate full-rank design matrix.

A simulation study shows that, besides the Bayesian D-optimal ranking design, the Bayesian D-optimal choice design is also an appropriate design for this type of experiments.

In contrast to fixed linear models, genome-wide prediction methods can also be applied to over-parametrized data sets with non full-rank design matrices.