The scenarios studied can be divided into two different groups: (1) The modeler assumes the data are error-free when they are not; the attempt to reproduce these erroneous data yields biases and artifacts in the final conditional models.
They extend the simple deterministic Guttman scaling model [ 3] which unrealistically assumes that the data are error-free.
Wherever possible, original employment records related to the establishment of the cohort were reviewed to ensure that the abstraction and computerization of the data were error free.
The results indicate that when the measured data is error-free, the proposed technique is very efficient in finding the exact damage locations and extents of both damage examples.
If the QTL is true, the effects estimate is true, and the sequence data are error-free, one of the observed private SNPs must be the causal variant for the QTL.
However, since the GPS data is much more accurate than the RSU data, it is assumed that the GPS data is error-free in order to get an acceptable filter parameter estimation.
It is worth noting that when the simulated RNA-Seq data is error-free, mapping tools may still result in incorrectly mapped reads and thus the input to Cufflinks/ISP could still be noisy.
Initially, assuming that the model and boundary condition data are error-free, daily in-river phosphorus concentration data are synthesised, representing an idealised system response.
A major challenge for applying GWAS to nonmodel species has been generating dense genome-wide markers that satisfy the key requirement that marker data are error-free.
Here, to study the potential advantage of sequence- vs. array-based genomic selection, we adopted an optimistic view: we assumed that SNP genotypes obtained from NGS data were error-free, without missing data, and that as many individuals can be sequenced as genotyped and phenotyped.
When data are error free, our algorithm is not the first O(nlog n) algorithm, as Kannan et al.[ 14] have given a rooted-triple algorithm with this runtime.
