Scent stations usually are deployed in clusters to expedite data collection and increase the number of stations that can be operated for a given cost. Presumed benefits of cluster sampling may not be realized, however, unless cluster sizes are chosen with respect to sampling variation within and among clusters. To encourage and facilitate the use of efficient designs and reporting standards, we...

Markov chain Monte Carlo (MCMC) methods have been used in many fields (physics, chemistry, biology, and computer science) for simulation, inference, and optimization. The essence of these methods is to simulate a Markov chain whose state X follows a target probability X ∼ π(X). In many applications, π(X) is defined on a graph G whose vertices represent elements in the system and whose edges rep...

When disease incidence locations are observed in a region, there is often interest in studying whether there is clustering about landmarks representing possible centralized sources of the disease. In this article we study a Bayesian approach to the detection and estimation of such landmarks. Spatial point processes are used to specify both the observation process and the prior distribution of t...

For better inference of the population quantity of interest, ratio estimators are often recommended when certain auxiliary variables are available. Two types of ratio estimators, modified for adaptive cluster sampling via transformed population and initial intersection probability approaches, have been studied in Dryver and Chao (2007). Unfortunately, none of them are a function of a minimal su...

FRANCIS A. ROESCH, JR. Adaptive cluster sampling is shown to be a viable alternative for sampling forests when there are rare characteristics of the forest trees which are of interest and occur on clustered trees. The ideas of recent work in Thompson (1990) have been extended to the case in which the initial sample is selected with unequal probabilities. An example is given in which the initial...

Abstract In this research work we introduce a new sampling design, namely two-stage cluster sampling, where probability proportional to size with replacement is used in the first stage unit and ranked set second order address issue of marked variability sizes population units concerned sampling. We obtained an unbiased estimator mean total, as well variance estimator. calculated relative effici...

Cluster computation power provides a promising way to improve response time in large data warehouses. On the other hand, the use of sampling summaries on the cluster for approximate answering of OLAP queries provides a very flexible system that can provide response time guarantees. In this paper we explore the cluster computation paradigm for data warehouses and summaries. The use of cluster co...

In many situations there is interest in parameters (e.g., mean) associated with the response distribution of individual clusters in a finite clustered population. We develop predictors of such parameters using a two-stage sampling probability model with response error. The probability model stems directly from finite population sampling without additional assumptions and thus is design-based. T...

Multilevel regression with poststratification (MRP) has become widely used in political science to estimate subnational opinion from national polls. This method takes into account both demographic and state-level effects and has greatly expanded knowledge of public opinion at the subnational level. In this paper, I assess the use of MRP on two cluster-sampled polls, the American National Electi...

This paper presents a SVM classification method based on cluster boundary sampling and sample pruning. We actively explore an effective solution to solve the difficult problem of imbalanced data set classification from data re-sampling and algorithm improving. Firstly, we creatively propose the method of cluster boundary sampling, using the clustering density threshold and the boundary density ...