The purpose of this article is to introduce a new scheme for robust multivariate ranking by making use of a not so familiar notion called monotonicity. Under this scheme, as in the case of classical outward ranking, we get an increasing sequence of regions diverging away from a central region (may be a single point) as nucleus. The nuclear region may be deened as the median region.

15. Supplementary Notes Supported by general revenues from the State of Texas 16. Abstract In recent years, there has been a renewed interest in applying statistical ranking criteria to identify sites on a road network, which potentially present high traffic crash risks or are overrepresented in certain type of crashes, for further engineering evaluation and safety improvement. This requires th...

Regarding the fact that getting a suitable combination of the human resources and service stations is one of the important issues in the most service and manufacturing environments, In this paper, we have studied the two models of planning queuing systems and its effect on the cost of the each system by using two fuzzy queuing models of M/M/1 and M/E2/1. In the first section, we have compar...

Recent research efforts have led to the development of a state-of-the-art supervised coreference model, the cluster-ranking model. However, it is not clear whether the features that have been shown to be useful when employed in traditional coreference models will fare similarly when used in combination with this new model. Rather than merely re-evaluate them using the cluster-ranking model, we ...

We develop a multi-period theoretical model to characterize the relationship between a publication that ranks universities and prospective attendees – high school students – who might view the ranking and use it to help decide which university to attend. We assert that published rankings not only offer information about the objective quality of universities, but also have an effect on the prest...

In this work, we exploit the regression trees-based ranking model, which has been successfully applied in the domain of web-search ranking, to build appearance models for face alignment. The model is an ensemble of regression trees which is learned with gradient boosting. The MCT (Modified Census Transform) as well as its unbinarized version PCT (Pseudo Census Transform) are used as features du...

With the rapid development of Internet and E-commerce, the quantity of product reviews on the web grows very fast, but the review quality is inconsistent. This paper addresses the problem of automatically ranking reviews. A specification for judging the reviews quality is first defined and thus ranking review is formalized as ordinal regression problem. In this paper, we employ Ranking SVM as t...

In this paper, we use an input oriented chance-constrained DEA model withrandom inputs and outputs. A super-eciency model with chance constraintsis used for ranking. However, for convenience in calculations a non-linear deterministicequivalent model is obtained to solve the models. The non-linearmodel is converted into a model with quadratic constraints to solve the nonlineardeterministic model...

data envelopment analysis (dea) is a mathematical programming method in operations research that can be used to distinguish between efficient and inefficient decision making units (dmus). however, the conventional dea models do not have the ability to rank the efficient dmus. this article suggests bootstrapping method for ranking measures of technical efficiency as calculated via non-radial mod...

Data Envelopment Analysis (DEA) is one of the popular and applicable techniques for assessing and ranking the stocks or other financial assets. It should be noted that in the financial markets, most of the times, the inputs and outputs of DEA models are accompanied by uncertainty. Accordingly, in this paper, a novel Robust Data Envelopment Analysis (RDEA) model, which is capable to be used in t...