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Finally, homogeneity of regression slopes was assessed; for some analyses for the ITT sample this assumption was violated.
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In other words, the received signal is stationary with the observation time T (i.e., N s samples); this assumption is commonly used in the literature [29 32].
When stratification, cluster sampling or probability weights are introduced into sampling this assumption is violated and the bootstrap as described above will give incorrect inferences.
However, for individual studies with sufficient samples, this assumption can be relaxed by using gene-specific variances s instead of in (1).
The fact that the association between moderate/severe periodontitis and psoriasis was attenuated when smoking was entered into the regression model for the whole sample supports this assumption.
However, with our reduced sample size, this assumption is not supported: half of the cases of compensation are associated to SE or RSE while the other half corresponds to cases of ME.
Manual inspection of samples confirmed this assumption.
Currently the most common estimation methods assume observations are sampled using a probabilistic sampling scheme, however this assumption is often not met.
This supposes a relatively uniform distribution of abundances across the entire sample, but if this assumption is false, the results might be even more interesting.
The calculation of CIQs has traditionally been based on the large-sample assumption that the quantile estimators are normally distributed, but with small samples commonly available, this assumption is shown to be quite crude.
Tumor samples may violate this assumption, frequently displaying genomes that are largely polyploid due to large scale aberrations in the mitotic mechanism, such as endoreduplication.
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