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.

 In this research, a new control policy for the acceptance sampling problem is introduced. Decision is made based on the number of defectives items in an inspected batch. The objective of the model is to find a constant control level that minimizes the total costs, including the cost of rejecting the batch, the cost of inspection and the cost of defective items. The optimization is performed by...

The creative universe is constantly expanding and so is its dimensionality with new forms of arts and entertainment, new technologies and new scientific specialties. Legal and economic conditions easily get turbulent in the drag of this expansion process, creating all sorts of idiosyncracies, puzzles and paradoxes surrounding legal rights and economic values associated with creators and their c...

Reliable estimates of abundance are needed to assess consequences of proposed habitat restoration and enhancement projects on freshwater mussels in the Upper Mississippi River (UMR). Although there is general guidance on sampling techniques for population assessment of freshwater mussels, the actual performance of sampling designs can depend critically on the population density and spatial dist...

THE EFFECT OF NONRANDOM SELECTION OF CLUSTERS IN A TWO STAGECLUSTER DESIGN byJASON PARROTTDecember 2012Advisor: Dr. Shlomo SawilowskyMajor: Evaluation and ResearchDegree: Doctor of Philosophy Although not as efficient as simple random sampling, cluster sampling has beenregarded as a valid sampling technique when the researcher is attempting to save cost. However,...

Due to the explosive growth of data in every aspect of our life, data mining algorithms often suffer from scalability issues. One effective way to tackle this problem is to employ sampling techniques. This paper introduces, ML-DS, a novel deterministic sampling algorithm for mining association rules in large datasets. Unlike most algorithms in the literature that use randomness in sampling, our...

PAUL ALAN FLYER. Finite Population Correction for Replication Estimates of Variance. (Under the direction of CHIRAYATH M. SUCHINDRAN and LESTER R. CURTIN.) For large, national surveys, stratified two stage sample designs are frequently used. In these surveys, unequal probability sampling is almost always used in the selection of first stage units. Typically, the sampling procedure does not allo...

This paper deals with estimation of probabilities of rare events in static simulation models using a fast adaptive two-stage procedure based on importance sampling and Kullback-Liebler’s cross-entropy (CE). More specifically, at the first stage we estimate the optimal parameter vector in the importance sampling distribution using CE, and at the second stage we estimate the desired rare event pr...

1.0 Introduction Replication variance estimation for a two-stage nested sample design is usually implemented by generating replicate samples (weights) that replicate the original first-stage sample selection. Since the second-stage is nested, the second-stage variance can be reflected by associating each second-stage unit with its respective first-stage unit in each firststage replicate sample....