نتایج جستجو برای: negative data
تعداد نتایج: 2824401 فیلتر نتایج به سال:
these days, all department stores make an effort to provide their clients with valuable products in order to project the best image for them. as a result, clients’ comprehension risk will decrease and they will be more willing to repurchase. having a good image is really important for the department stores because it makes an impression on clients’ comprehension of both quality and risk. consid...
data envelopment analysis (dea) is a technique based on mathematical programming for evaluating the efficiency of homogeneous decision making units (dmus). in this technique, inefficient dmus are projected onto the frontier, which was constructed by the best performers. centralized resource allocation (cra) is a method in which all dmus are projected onto the efficient frontier through solving ...
Super-efficiency model in the presence of negative data is a relatively neglected issue in the DEA field. The existing super-efficiency models have some shortcomings in practice. In this paper, a novel VRS radial super-efficiency DEA model based on Directional Distance Function (DDF) is proposed to provide a complete ranking order of units (including efficient and inefficient ones). The propose...
In this paper, it is assumed that the “Decision Making Units“( ) are consist of positive and negative input and output. Firstly, the optimistic and pessimistic models have been suggested by using negative data and then units with most productive scale size are measured in optimistic and pessimistic models. These productive values are compared with double frontiers and Hurwicz’s Criterion to obt...
Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
in this paper, it is assumed that the “decision making units“( ) are consist of positive and negative input and output. firstly, the optimistic and pessimistic models have been suggested by using negative data and then units with most productive scale size are measured in optimistic and pessimistic models. these productive values are compared with double frontiers and hurwicz’s criterion to obt...
One of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the negative selection methods consider deterministic boundaries to distinguish between self and non-self-spaces. In this paper, two...
The present study is an attempt toward evaluating the performance of portfolios using mean-variance-skewness model with negative data. Mean-variance non-linear framework and mean-variance-skewness non- linear framework had been proposed based on Data Envelopment Analysis, which the variance of the assets had been used as an input to the DEA and expected return and skewness were the output. C...
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