Random sampling is a popular technique for providing fast approximate query answers, especially in data warehouse environments. Compared to other types of synopses, random sampling bears the advantage of retaining the dataset’s dimensionality; it also associates probabilistic error bounds with the query results. Most of the available sampling techniques focus on table-level sampling, that is, t...

PURPOSE The quality and economy of inferential research rely heavily on the sampling method. This paper addresses a methodological challenge in environment-behavior research: sampling respondents in relation to their built environmental characteristics. METHODS A discussion of issues related to traditional neighborhood-based sampling serves to introduce a new spatial sampling strategy. Spatia...

Abstract Objectives Respondent-Driven Sampling (RDS) is a variant of link-tracing, sampling technique for surveying hard-to-reach communities that takes advantage community members' social networks to reach potential participants. While the RDS mechanism and associated methods adjusting at analysis stage are well-documented in statistical sciences literature, methodological focus has largely be...

The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is qui...

The mycotoxin ochratoxin A (OTA) is known to be heterogeneously distributed both intrinsically (from one individual food item to the next) as well as distributionally (throughout a sample of individual food items) in cereals and cereal-based foods. Therefore, proper sampling and sample comminution are special challenges, but are prerequisites for obtaining sound analytical data. This paper outl...