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Furthermore, we employ the jackknife repeated replication technique (using the variables JKZONE and JKREP) to estimate sampling errors and account for the complex stratified cluster samples in PIRLS (Lohr 2010; Rutkowski et al. 2010).
In the clustering step, the function in the CGPredictor package called "kmeans" is used to cluster samples.
However we do not find significant differences in the fraction of clusters with disturbed morphologies in the different cluster samples.
We find a significantly smaller fraction of cool-core clusters in the Planck sample than in X-ray selected cluster samples.
In this paper, we use a hierarchical Bayesian model to convert the observed cluster samples to simple random samples.
In order to construct accuracy metamodel, the fuzzy based progressive metamodeling method was implemented to cluster samples generated by BBNS several patches in optimization domain.
First, the within-cluster correlations in fish lengths among cluster samples collected from commercial fisheries or simulated based on different sampling designs were evaluated.
We put together the results of XMM-Newton observations of a number of representative group and cluster samples at low and high redshifts.
A strong correlation in fish length among vermilion snapper cluster samples was found, which significantly decreased the precision in mean lengths and caused a several fold increase in the confidence interval for total mortality.
Since it is expressed as differences of the correlation among measured variables, it is useful to cluster samples on the basis of the correlation among variables for adopting a multi-model approach.
We demonstrate the utility of our procedure using an example and also provide a simulation study which establishes our methodology as a viable alternative to the Rao Scott approximations for relatively small two-stage cluster samples.
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