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Cheng et al. used sensitivity analyses to compare different methods (six models for clustered binary outcomes and three models for clustered nominal outcomes) of analysing correlated data in discrete choice surveys [ 49].
J.M. Neuhaus and N. P. Jewell, "The effect of retrospective sampling on binary regression models for clustered data," Biometrics, 46, 1990, 977-990.
In this paper we study properties of the estimators of marginal mean parameters in the GEE1 approach of Heagerty and Zeger [Heagerty, P.J., Zeger, S.L., 1996. Marginal regression models for clustered ordinal measurements. J. Amer. Statist. Assoc. 91, 1024 1036] for clustered ordinal data.
If there are important confounding variables, stratification, matching or regression models for clustered data are required.
Risk factors for SIEs were investigated by logistic regression models for clustered data (Table 3).
Investigating the impact of the distribution of random effects on parameter estimation in mixture models for clustered semi continuous data will be taken up as future research.
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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.
Bayesian models for clustering of genotype data use the framework of mixture models to model individuals.
J.M. Neuhaus and N. P. Jewell, "Some comments on Rosner's multiple logistic model for clustered data," Biometrics, 46, 1990, 523-531.
In[11], an energy model for clustered multi-hop WSNs is derived and optimized with respect to the cluster-head selection process, hereby using a probabilistic method.
The preliminaries and network model are stated in Section 3. The proposed secure clustering scheme is presented in Section 4. Section 5 shows an analysis on node degree in the proposed network model for clustered WSNs.
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