The goal of importance sampling is to estimate the expected value of a given function with respect to a probability measure ν using a random sample of size n drawn from a different probability measure μ. If the two measures μ and ν are nearly singular with respect to each other, which is often the case in practice, the sample size required for accurate estimation is large. In this article it is...

A total of 24 commercial fields of cabbages and Brussels sprouts were sampled in a grid fashion with 20-25 equally spaced cells with four plants per cell. Using this data base of 80-100 plants, we conducted computer stimulations to compare the treatment decisions that would be made for the major insect pests using published sequential sampling programs and a newly developed variable-intensity s...

Two-locus sampling probabilities have played a central role in devising an efficient composite-likelihood method for estimating fine-scale recombination rates. Due to mathematical and computational challenges, these sampling probabilities are typically computed under the unrealistic assumption of a constant population size, and simulation studies have shown that resulting recombination rate est...

This paper illustrates some of the many sampling algorithms built into PROC SURVEYSELECT, particularly those pertinent to complex surveys, such as systematic, probability proportional to size (PPS), stratified, and cluster sampling. The primary objectives of the paper are to provide background on why these techniques are used in practice and to demonstrate their application via syntax examples....

The main purpose of this article is to show how one can integrate statistical and nonstatistical items of evidence under the belief function framework. First, we use the properties of consonant belief functions to define the belief that the true mean of a variable lies in a given interval when a statistical test is performed for the variable. Second, we use the above definition to determine the...