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This is done in order to avoid different boundary conditions between sample and model.
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The chi-square fit index is highly sensitive to sample size and the hypothesized model is likely to be rejected when the sample size is large, even though the discrepancy between the sample and model covariance matrices may be small (Fan et al. 1999; Fan and Wang 1998).
Different features are observed between the sorption samples and model compounds.
The estimated FDR for the GV-MII was 0.03 using the between replicate null model and 0.33 using the between sample null model.
This leads to the problem that plausible models might be rejected based on a significant χ statistic even though the discrepancy between the sample and the model-implied covariance matrix is irrelevant.
Therefore, it is reasonable to describe lag-phase by comparing the composition difference between sample t1 and t2 (model 1, 2 and 3 in Table 3).
The coincidence between random sampling and weighted model in Figure 9 is the same as described previously for the average variance of the estimates.
Every proper PCFG is consistent if it minimizes the cross‐entropy function between probability distributions of the sample and the model [ 105].
In the instance of insulin resistance and diabetes, our results here indicate the presence of some of the common features between human samples and mouse models.
3. The congruence of the loadings between the Danish sample and the consensus model were calculated, for both the whole model and each of the factors.
We model the pdfs using GMMs or HMMs and then calculate the distance between samples from the model parameters.
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