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This article examines the predictions of self-similar models for clusters, which come in several different flavors, proposes some alternative scaling laws that account for radiative cooling and feedback in very a simplistic way, and compares those laws with the existing data.
Subsequently, the authors provide estimations of different probit models for clusters of NEETs identified according to the modalities of some significant determinants.
We computed the maximum distance between the all combinations of the fields in the cluster and fitted logistic regression models for clusters including females (excluding distances based on surname, because of the frequent changes in surname occurring on marriage), or for clusters without any females, modelling the odds of bad status relative to good.
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Magidson, J., Vermunt, J. K. (2002b) Latent class models for clustering: A comparison with K-means.
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points.
J.M. Neuhaus and N. P. Jewell, "The effect of retrospective sampling on binary regression models for clustered data," Biometrics, 46, 1990, 977-990.
Bayesian models for clustering of genotype data use the framework of mixture models to model individuals.
Risk factors for SIEs were investigated by logistic regression models for clustered data (Table 3).
Previously, similar ideas which use probability models for clustering have been applied to classify protein subgroups.
If there are important confounding variables, stratification, matching or regression models for clustered data are required.
These levels of genome-wide variation have not been fully incorporated into evolutionary models for cluster formation and speciation.
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