共查询到19条相似文献,搜索用时 156 毫秒
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无量纲化处理是开展综合评价的基础,目前线性无量纲化方法很少考虑群体评价的情况。本文针对常用的6种线性无量纲化方法直接应用到群体评价中不能保证各评价者评价信息横向大小顺序的问题,首先对问题进行界定,并对6种线性无量纲化方法进行了扩展;其次进一步分析了扩展后的线性无量纲化方法的性质,并针对群体评价问题引入“横向单调性”和“变量单一性”两个性质,为线性无量纲化方法的设计研究提供重要的参考;再次以无量纲化后的数据最大程度的保留原始信息为原则,针对不同的赋权方法,给出线性无量纲化方法选择的建议;最后,用一个算例检验了方法的有效性。 相似文献
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综合评价中数据变换方法的选择 总被引:1,自引:0,他引:1
为了消除不同评价指标中不同的量纲和量纲单位带来的不可公度性,本文讨论了综合评价中数据变换方法的选择问题.根据序号总和理论,本文将各评价指标作无量纲化处理,根据各无量纲化方法所做的等级排序与合理等级排序的"距离"进行无量纲化方法的优选,并通过实例分析得出了相应的结论.均值化方法保留了各指标变异程度的信息,是一种较好的数据处理方法. 相似文献
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针对线性无量纲化方法对群体评价中信息集结结果的影响问题,本文以线性加权的群体信息集结方法为背景,以集结成的群体信息最大程度地扩大被评价对象间的差异为导向,给出了群体信息集结过程中无量纲化方法选择的若干结论和建议。首先设定评价情景并提出研究假设,分析不同无量纲化方法集结成的群体信息对各被评价对象间的差异影响;然后对造成被评价对象之间差异的主要因素进行了讨论;通过对不同因素的分析以及与未经过无量纲化处理集结成的群体信息中各被评价对象间差异的比较,得出一些重要的结论,并给出一些针对群体信息结集过程中选择无量纲化方法的建议;最后,用一个算例检验了结论的有效性。 相似文献
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线性无量纲化方法的对比及反向指标的正向化方法都是综合评价的重要研究内容。从指标差异信息的角度,以TOPSIS、基于街区距离的TOPSIS和线性加权综合法为例,基于理论推导和实证分析对比了常用的线性无量纲化方法,并提出了两种反向指标正向化方法。研究发现,对于线性加权综合法和TOPSIS,不同线性无量纲化方法下同一指标归一化极差的不同是导致排序结果存在差异的关键因素;本文提出的反向指标正向化方法,不仅可以保证正向化前后TOPSIS、基于街区距离的TOPSIS的评价值不变,也可以实现反向指标正向化后线性加权综合法与基于街区距离的TOPSIS在排序目的上的等效性。最后,本文提出了线性无量纲化方法和反向指标正向化方法的应用建议。 相似文献
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探讨了半带状区域上二维Poisson方程只含有一个空间变量的热源识别反问题.这类问题是不适定的,即问题的解(如果存在的话)不连续依赖于测量数据.利用Carasso-Tikhonov正则化方法,得到了问题的一个正则近似解,并且给出了正则解和精确解之间具有Holder型误差估计.数值实验表明Carasso-Tikhonov正则化方法对于这种热源识别是非常有效的. 相似文献
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探讨半无界区域上二维修正的Helmholtz方程只含有一个空间变量的未知源识别反问题.这类问题是不适定的,即问题的解(如果存在的话)不连续依赖于测量数据.利用Fourier截断正则化方法,得到问题的一个正则近似解,并且给出正则解和精确解之间收敛的误差估计.数值例子表明Fourier截断正则化方法对于这种未知源识别非常有效. 相似文献
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选取合适的系统和评价指标,对所收集的数据进行无量纲化的处理,然后用“纵横向”拉开档次法对处理后的数据进行分析得到权重系数,建立综合评价模型并进行评价,最后用最大序差对评价结果进行分析总结. 相似文献
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对于多属性群决策问题的处理,有时需要采用先决策、后综合的处理方法,而含有语言评价信息的多属性群决策问题,定性目标一般用语言评价信息描述,由决策人给出定性目标和权系数的语言变量评价,用梯形模糊数表示,对定量目标进行无量纲化处理;将决策人对于单一目标的评价指标聚合成多个目标的评价模糊数,采用Bass-Kw akernaak模糊数排序方法对方案进行排序;群体的评价通过Borda函数来集结方案集的群体排序. 相似文献
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异常观测数据的逐点剔除法 总被引:6,自引:0,他引:6
本文通过引进一类特殊的统计量和对其统计性质的分析,得到了一种线性回归函数因变量异常观测数据的逐点剔除方法,该方法能很快地认识和剔除含粗大的误差的观测数据,具有低的误判概率。本文还给出了与本文方法配套的数值算法,说明本文方法的数值实例和仿真结果。 相似文献
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本文讨论了在指数型寿命数据中,对同时存在的异常大数据和异常小数据的检验方法,给出了一个明确的判别标准,并以一例说明其应用。 相似文献
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《European Journal of Operational Research》2002,136(3):603-615
Popularity of nontraditional approaches to the statistical classification problem has resulted from the potential of these techniques to outperform the standard parametric procedures under conditions when nonnormality is present. Thus proponents of these nontraditional models have recommended these models when outliers are in the data. However, research showing that these nontraditional models' performances can vary widely depending on where the outlier data are located has not been fully illustrated. The research in this paper demonstrates how the mathematical programming approaches and the nearest neighbor discriminant models can be affected by the position of contaminated normal data and that each of the models studied in this paper may not be robust to all types of outliers in the data. The results of this paper are also important because the study compares two recently proposed mathematical programming models as well as two versions of the nearest neighbor model with the standard classical parametric models. This combination of classification models does not appear to have been studied together under conditions of contaminated normal data in which numerous positions of the outliers are considered. 相似文献
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Kangning Wang 《Annals of the Institute of Statistical Mathematics》2018,70(2):323-351
Spatial semiparametric varying coefficient models are a useful extension of spatial linear model. Nevertheless, how to conduct variable selection for it has not been well investigated. In this paper, by basis spline approximation together with a general M-type loss function to treat mean, median, quantile and robust mean regressions in one setting, we propose a novel partially adaptive group \(L_{r} (r\ge 1)\) penalized M-type estimator, which can select variables and estimate coefficients simultaneously. Under mild conditions, the selection consistency and oracle property in estimation are established. The new method has several distinctive features: (1) it achieves robustness against outliers and heavy-tail distributions; (2) it is more flexible to accommodate heterogeneity and allows the set of relevant variables to vary across quantiles; (3) it can keep balance between efficiency and robustness. Simulation studies and real data analysis are included to illustrate our approach. 相似文献
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Ranking fuzzy numbers is often a necessary step in many mathematical models, and a large number of ranking methods have been proposed to perform this task. However, few comparative studies exist and nowadays it is still unknown how similar ranking methods are in practice, i.e., how likely they are to induce the same ranking. In this study, by means of numerical simulations, we try to answer this question. We shall discover that there are some very similar methods as well as some outliers. We end the paper interpreting the results and giving some recommendations on the use of ranking methods. 相似文献