We describe an extension of the pseudo maximum likelihood (PML) estimation method developed by Skinner (1989) to multistage strati ̄ed cluster sampling designs, including ̄nite population and unequal probability sampling. We conduct simulation studies to evaluate the performance of the proposed estimator. The estimator is also compared to the general estimating equation (GEE) method for linear r...

A brief introduction to the technique of Monte Carlo for simulation of statistical physics problems is presented. Ising spin model is taken as an example. The topics covered include random and pseudo random numbers, random sampling techniques, Markov chain, Metropolis algorithm, continuous phase transition, statistical errors from correlated and uncorrelated data, finite size scaling, critical ...

Studnicki M., Mądry W., Schmidt J. (2013): Comparing the efficiency of sampling strategies to establish a representative in the phenotypic-based genetic diversity core collection of orchardgrass (Dactylis glomerata L.). Czech J. Genet. Plant Breed., 49: 36–47. Establishing a core collection that represents the genetic diversity of the entire collection with a minimum loss of its original divers...

BACKGROUND The purpose of this study was to estimate the bias and design effects associated with the Expanded Program on Immunization's (EPI) sampling design when estimating xerophthalmia prevalence, and to estimate the savings associated with EPI in terms of distance travelled within selected clusters. METHODS Computer simulation of the EPI sampling strategy was done using maps from a xeroph...

In nature, populations are sometimes distributed in a patchy, rare, or aggre-gated manner. Conventional sampling designs such as simple random sampling (SRS) do not take advantage of this spatial differentiation. Thompson (1990) introduced a sampling design called adaptive cluster sampling (ACS) to survey these types of distributions. Adaptive cluster sampling, in theory, can be much more preci...

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Best linear unbiased predictors developed from mixed models are widely used, but their development requires distributional assumptions or an infinite population frame...

Recently, Zhou and Robles-Kelly proposed a novel quasi-random sampling (QuaRS) approach for CBIR [4]. This approach uses EM algorithm to organize the images in the database into compact clusters, then compares the similarity between the query and the clustered images to govern the sampling process within clusters. The sampling can be viewed as a stratified sampling one which is random at the cl...

A weighted cluster sample survey design is frequently used in large demographic sample surveys. In the National Health Interview Survey conducted by the National Center for Health Statistics, households are often selected in clusters of four. In this survey, sociodemographic and health characteristics of all members of sample households are recorded. Such characteristics for each person intervi...

SUMMARY The polya posterior gives a noninformative Bayesian justification for various single-stage sampling procedures. Here this type of reasoning is extended to two-stage cluster sampling problems. The frequency properties of the resulting procedures are studied.