Generating sample models for testing a model transformation is no easy task. This paper explores the use of classifying terms and stratified sampling for developing richer test cases for model transformations. Classifying terms are used to define the equivalence classes that characterize the relevant subgroups for the test cases. From each equivalence class of object models, several representat...

In this article, we propose compromise allocations for multivariate stratified random sampling using the auxiliary attributes under non-response. We modified extended lexicographic goal programming technique and compared it with fuzzy goal programming and value function technique. We addressed the problem of compromise allocation when the auxiliary information is in the form of an auxiliary att...

Producing large enough quantities of high-quality transcriptions for accurate and reliable evaluation of an automatic speech recognition (ASR) system can be costly. It is therefore desirable to minimize the manual transcription work for producing metrics with an agreed precision. In this paper we demonstrate how to improve ASR evaluation precision using stratified sampling. We show that by alte...

A critical problem in planning sampling paths for autonomous underwater vehicles is correctly balancing two issues. First, obtaining an accurate scalar field estimation and second, efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real-time and a priori environmental data is available. In this paper we present ...

Prentice & Pyke (1979) established that the maximum likelihood estimate of an odds ratio in a case-control study is the same as would be found by fitting a logistic regression; in other words, for this specific target the incorrect prospective model is inferentially equivalent to the correct retrospective model. Similar results have been obtained for other models, and conditions have also been ...

In this paper, for the Stratified Median Ranked Set Sampling (SMRSS), proposed by Ibrahim et al. (2010), we examine the proportional and optimum sample allocations that are two well-known methods for sample allocation in stratified sampling. We show that the variances of the mean estimators of a symmetric population in SMRSS using optimum and proportional allocations to strata are smaller than ...

Sampling is the process of collecting, analysing, and interpreting data in order to test hypotheses and provide needed information for intelligent decisions. Sampling is an attractive alternative to complete enumeration. Instead of measuring or recording all members of a population of interest, one concentrates on a representative proportion, objectively selected, to estimate desired characteri...

The main objective of this study is to come to know the fact that which level of the three levels of managers (Top, Middle and Low Level) has more participation in applying e-commerce in the organization. Another purpose of the study is to know that how many people are employed as IT personal in the Indian small and medium enterprises? In this study we used Stratified Random Sampling to collect...

This article considers Monte Carlo integration under rejection sampling or Metropolis-Hastings sampling. Each algorithm involves accepting or rejecting observations from proposal distributions other than a target distribution. While taking a likelihood approach, we basically treat the sampling scheme as a random design, and define a stratified estimator of the baseline measure. We establish tha...

This paper considers the design of acoustic surveys for estimating the mean abundance of spatially correlated populations. We examined how the choice of survey design affects the bias and precision of the sample mean as an estimator of mean abundance. Further, we investigated three different ways of estimating the error variance of the sample mean: the pooled within strata variance and two geos...