Estimates and simulated values agree very well; some bias for small values can be attributed to the EM-REML algorithm used for variance component estimation.
This often results in the inflation of small values, which can have an undesirable side effect as the influence of the measurement error, that is usually relatively large for small values, is increased as well.
In particular, for small values of time we recover the well-known Washburn law.
For small values of the thresholds, it has been observed that the number of detections increases, but the new detections did not overlap well.
We also improve this bound for small values of d.
We have used both the 0.7 and 0.8 values, as well as small values (0.3, 0.4) for ρ and found no qualitative difference in the results.
It is found that an exponential PDF works well for low values of scalar dissipation rate and smaller filter sizes whereas a lognormal PDF works well for larger values of scalar dissipation rate and larger filter sizes.
Genomic prediction models that fit well have small values for c and result in greater relative emphasis of reliable information than is the case when the genomic prediction model fits poorly and the residual variation is dominated by contributions from lack-of-fit.
Even if the distribution of optimal scores is of unknown form for intermediate values of N, the empirical observations show that normal approximations fit well except for very small values.
This works well for small adjustments.
Additionally, calculated effect sizes (Cohen's d, [ 53]) for differences between children with or without DD as well as for gender differences ranged from small values to medium values for both effects (with or without DD: min. d =.08 (EX RT), max.
