Objective: Determining which subgroups show the most substantial differences on a measure is a common use of surveys. How to accurately and fairly determine which subgrouping is most important has not been addressed adequately in the literature. I show how dominance analysis is a useful way to identify the most important subgroup differences. Because surveys commonly employ complex sampling des...

It is nearly 40 years since an earlier survey on the use of distributional weights in Cost-Benefitcost–benefit analysis was first published. Since then, a number significant contributions have been made literature. therefore timely to revisit issue by focusing key reasons why are so necessary. We present new justification for using unequal income CBA, and reaffirm completely neglected reason ad...

We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision ma...

Functional connectivity analysis yields powerful insights into our understanding of the human brain. Group-wise functional community detection aims to partition the brain into clusters, or communities, in which functional activity is inter-regionally correlated in a common manner across a group of subjects. In this article, we show how to use multiple-view spectral clustering to perform group-w...

background: mixed infection with amphistomes seems common in native cattle of iran. the aim of this study was to determine diagnostic antigens in cattle mixed amphistomiasis. methods: specific antigens of cotylophoron cotylophorum , gastrothylax crumenifer and paramphisto­mum cervi (mixed infection), the most common species, were collected from cattle was deter­mined. adult trematodes were coll...

It is a common procedure for scattered data approximation to use local polynomial fitting in the least-squares sense. An important instance is the Moving Least-Squares where the corresponding weights of the data site vary smoothly, resulting in a smooth approximation. In this paper we build upon the techniques presented by Wendland and present a somewhat simpler error analysis of the MLS approx...

This paper considers single machine due-window assignment scheduling problems with position-dependent weights. Under the learning and deterioration effects of jobs processing times, our goal is to minimize weighted sum earliness-tardiness, starting time due-window, size, where weights only depends on their position in a sequence (i.e., weights). common (CONW), slack (SLKW) different (DIFW) assi...