共查询到18条相似文献,搜索用时 171 毫秒
1.
从上海市某区386家中小企业申报的15项税收指标数据中筛选出对判定企业纳税情况具有重要影响的10个评价指标,并将全部386个样本分成性质相似的建模样本和测试样本(其中测试样本个数占45%),建立了基于投影寻踪分类(PPC)技术的税务稽查评价模型.与多元线性回归(MLR)、判别分析(MDA)、Logistic和支持向量机(SVM)模型相比,PPC模型的识别错误率最低,建模样本和测试样本的平均分类错误率低于6%,改进型PPC模型包含的评价指标少,两类错误率很接近,非常适用于实际企业的税务稽查评估研究和实践.对339家待判断企业纳税情况的判定结果研究表明,建立的改进型PPC模型具有很好的泛化能力和鲁棒性. 相似文献
2.
传统线性模型异常点识别方法容易发生误判:正常点被归为异常点或者异常点被归为正常点.为解决此类问题,提出了应用逆跳马尔科夫蒙特卡洛方法识别异常点的思想,同时将其应用于实际数据加以检验,识别效果明显好于传统方法. 相似文献
3.
4.
叶艺勇 《数学的实践与认识》2014,(1)
纳税评估是税务稽查的基础和前提,运用科学、合理的评估手段,降低评估风险和成本,提高纳税评估工作的整体效率和效果,成为纳税评估工作的关键问题.在总结纳税评估基本原理的基础上,针对企业的纳税评估问题构建了评估指标体系,提出了将支持向量机(SVM)和领域知识结合建立纳税评估预警模型的方法,首先利用SVM筛选出有涉税疑点的企业,然后在领域知识的指导下结合统计分析方法,进一步找出可疑对象偏离正常统计值的指标,解决了预警模型无疑点指向性的问题,实证分析表明该方法具有较高的可行性. 相似文献
5.
6.
7.
《数学的实践与认识》2013,(15)
小波矩特征法是一种既能反映图像的全局信息,又能反映局域信息,并且具有旋转、平移和缩放不变形的图像特征提取方法,在模式识别中能大大提高对近似目标识别的能力.为保证油井正常生产,精确快速地诊断出有杆抽油系统的工况,基于小波矩特征和自组织竞争神经网络(SCNN)建立了一种泵功图诊断模型.利用Matlab将悬点示功图转化为泵功图,然后基于小波矩特征法提取泵功图的小波矩特征并优选出合适的小波矩特征量,使用SCNN识别诊断.仿真研究表明,模型能够可靠的诊断有杆抽油系统的工况,实现简单,准确率高,适用于现场诊断,具有一定的工程应用前景. 相似文献
8.
基于改进的Cholesky分解,研究分析了纵向数据下半参数联合均值协方差模型的贝叶斯估计和贝叶斯统计诊断,其中非参数部分采用B样条逼近.主要通过应用Gibbs抽样和Metropolis-Hastings算法相结合的混合算法获得模型中未知参数的贝叶斯估计和贝叶斯数据删除影响诊断统计量.并利用诊断统计量的大小来识别数据的异常点.模拟研究和实例分析都表明提出的贝叶斯估计和诊断方法是可行有效的. 相似文献
9.
10.
信用卡套现是一种威胁正常金融秩序的风险行为。有效识别具有套现风险的商户及其风险交易,对信用卡风控具有重要意义。传统的信用卡风险识别方法需要先积累大量的标注数据,对持卡人拥有充分的先验信息。而信用卡标注数据的稀缺大大限制了传统方法的应用。本文充分挖掘海量交易流水数据,提出基于无监督学习的套现交易及风险商户识别方法。一方面,该方法无需关于信用卡的标注数据或先验信息,能够以数据驱动的方式过滤行为异常的套现交易及风险商户,具有更广泛的应用前景。另一方面,该方法综合商户的交易金额属性,及商户与消费者之间的关联关系,构建出一系列可解释性强的套现风险指标,为风控管理提供直观的指导参考。基于某第三方支付平台实际数据的实证分析表明,本文方法能够有效区分具有不同行为表现、不同风险等级的商户群体,为实际的套现交易识别提供可靠的决策支持。 相似文献
11.
This paper explains some drawbacks on previous approaches for detecting influential observations in deterministic nonparametric data envelopment analysis models as developed by Yang et al. (Annals of Operations Research 173:89–103, 2010). For example efficiency scores and relative entropies obtained in this model are unimportant to outlier detection and the empirical distribution of all estimated relative entropies is not a Monte-Carlo approximation. In this paper we developed a new method to detect whether a specific DMU is truly influential and a statistical test has been applied to determine the significance level. An application for measuring efficiency of hospitals is used to show the superiority of this method that leads to significant advancements in outlier detection. 相似文献
12.
2018年实施的《环境保护税法》确立了排污征税的法律依据。环境保护税会增加成本并沿着供应链向下游逐级转移,从而改变社会分配格局。对此,基于供应链视角研究环境保护税的社会分配效应:首先,分析环境保护税沿着供应链的逐级转移,发现环境保护税会同时减少企业利润和消费者剩余;其次,比较企业利润和消费者剩余的减少程度,发现环境保护税会提高消费者的社会分配地位,也会提高零售企业的社会分配地位,但是会降低制造企业的社会分配地位;最后,比较环境保护税减少的企业利润和消费者剩余与形成的税收收入之间的相对大小,发现环境保护税会导致净社会福利损失,而且税负分担非常不均衡,制造企业承担最多,零售企业次之,消费者最少。 相似文献
13.
In the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results, particularly for two-stage analyses. In the DEA-related literature, prior work on this issue has focused on the efficient frontier as a basis for detecting outliers. An iterative approach for dealing with the potential for one outlier to mask the presence of another has been proposed but not demonstrated. This paper proposes using both the efficient frontier and the inefficient frontier to identify outliers and thereby improve the accuracy of second stage results in two-stage nonparametric analysis. The iterative outlier detection approach is implemented in a leave-one-out method using both the efficient frontier and the inefficient frontier and demonstrated in a two-stage semi-parametric bootstrapping analysis of a classic data set. The results show that the conclusions drawn can be different when outlier identification includes consideration of the inefficient frontier. 相似文献
14.
高质量的决策越来越依赖于高质量的数据挖掘及其分析,高质量的数据挖掘离不开高质量的数据.在大型仪器利用情况调查中,由于主客观因素,总是致使有些数据出现异常,影响数据的质量.这就需要通过适用的方法对异常数据进行检测处理.不同类型数据往往需要不同的异常值检测方法.分析了大型仪器利用情况调查数据的总体特点、一般方法,并以国家科技部平台中心主持的"我国大型仪器资源现状调查"(2009)中大型仪器使用机时和共享机时数据为主线,比较研究了回归方法、基于深度的方法和箱线图方法等对不同类型数据异常值检测的适用性.选取不同角度,检验并采用不同的适用方法,找出相关的可疑异常值,有助于下一步有效开展大型仪器利用情况异常数据的分析处理,提高数据质量,为大型仪器利用情况综合评价奠定基础,也为科技资源调查数据预处理中异常值检测方法提供有益借鉴. 相似文献
15.
Summary The problem of detection of multidimensional outliers is a fundamental and important problem in applied statistics. The unreliability
of multivariate outlier detection techniques such as Mahalanobis distance and hat matrix leverage has led to development of
techniques which have been known in the statistical community for well over a decade. The literature on this subject is vast
and growing. In this paper, we propose to use the artificial intelligence technique ofself-organizing map (SOM) for detecting multiple outliers in multidimensional datasets. SOM, which produces a topology-preserving mapping of
the multidimensional data cloud onto lower dimensional visualizable plane, provides an easy way of detection of multidimensional
outliers in the data, at respective levels of leverage. The proposed SOM based method for outlier detection not only identifies
the multidimensional outliers, it actually provides information about the entire outlier neighbourhood. Being an artificial
intelligence technique, SOM based outlier detection technique is non-parametric and can be used to detect outliers from very
large multidimensional datasets. The method is applied to detect outliers from varied types of simulated multivariate datasets,
a benchmark dataset and also to real life cheque processing dataset. The results show that SOM can effectively be used as
a useful technique for multidimensional outlier detection. 相似文献
16.
17.
该文研究平衡单向分类随机效应模型中多个异常值的检验问题. 在基于随机效应上的均值滑动模型下导出了似然比检验统计量, 并给出了其精确分布及水平异常值的检验过程. 在基于观测误差上的均值滑动模型下,利用得分检验统计量给出了多个异常值的检验过程. 相似文献
18.
Richard Esposito James K. Fox Dongyow Lin Kevin Tidemann 《Journal of computational and graphical statistics》2013,22(2):113-125
Abstract The massive flood of numbers in ongoing large-scale periodic economic and social surveys commonly leaves little time for anything but a cursory examination of the quality of the data, and few techniques exist for giving an overview of data activity. At the U.S. Bureau of Labor Statistics, a graphical and query-based solution to these problems has recently been adopted for data review in the Current Employment Statistics survey. Chief among the motivations for creating the new system were: (1) Reduce or eliminate the arduous paper review of thousands of sample reports by review analysts; (2) allow the review analysts a more global view of sample activity and at the same time make outlier detection less of a strain; and (3) present global views of estimates over time and among groups of subestimates. The specific graphics approaches used in the new system were designed to quickly portray both time series and cross-sectional aspects of the data, as these are both critical elements in the review process. The described system allows the data analysts to track down suspicious sample members by first graphically pinpointing questionable estimates, and then pinpointing questionable sample data used to produce those estimates. Query methods are used for cross-checking relationships among different sample data elements. Although designed for outlier detection and estimation, the data-representation methods employed in the system have opened up new possibilities for further statistical and economic uses of the data. The authors were torn between the desire for a completely automatic system of data review and the practical demands of an actual survey operating under imperfect conditions, and thus viewed the new system as an evolutionary advance, not as an ideal final solution. Possibilities opened up by the new system prompted some further thinking on finding an ideal state. 相似文献