Abstract This paper develops optimal designs when it is not feasible for every cluster to be represented in a sample as stratified design, by assuming equal probability two-stage sampling where clusters are small areas. The allocation methods surveys small-area estimates priority. We seek efficient allocations the aim minimize linear combination of mean squared errors composite area estimators ...

We review the class of species sampling models (SSM). In particular, we investigate the relation between the exchangeable partition probability function (EPPF) and the predictive probability function (PPF). It is straightforward to define a PPF from an EPPF, but the converse is not necessarily true. In this paper we introduce the notion of putative PPFs and show novel conditions for a putative ...

Density-based techniques seem promising for handling data uncertainty in uncertain data clustering. Nevertheless, some issues have not been addressed well in existing algorithms. In this paper, we firstly propose a novel density-based uncertain data clustering algorithm, which improves upon existing algorithms from the following two aspects: (1) it employs an exact method to compute the probabi...

Drinking alcoholic beverages in places such as bars and clubs may be associated with harmful consequences such as violence and impaired driving. However, methods for obtaining probabilistic samples of drivers who drink at these places remain a challenge--since there is no a priori information on this mobile population--and must be continually improved. This paper describes the procedures adopte...

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. Recently, Stanek and Singer (JASA, 2004) developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed model...