基于标准模型库的企业财务风险分析方法探析

笔者在对企业财务风险指标进行科学选取的基础上 ,利用主成分分析法和因子分析方法计算企业财务风险综合得分 ;依照所得的两种综合得分进行风险聚类 ;并以聚类结果构建企业财务风险标准模型库。该风险标准模型库形成了企业财务风险的相对静态数据参考 ,据此 ,监管部门可方便地运用模糊识别方法对企业财务数据进行风险判断和分类 ,不需对加大样本的企业数据进行重新计算 ,大大提高了工作效率。该方法综合了几种方法的优点 ,在实践中得到了较合理的分析结果。     

成都市生活垃圾分类支付意愿及影响因素的问卷调查分析

为了为成都市垃圾分类政策制定提供科学依据,文章基于问卷调研,运用条件价值评估法结合二元Logistic回归模型、多元线性回归模型,分析了成都市居民对城市生活垃圾分类的支付意愿(WTP)及其影响因素。调研发现:问卷填报者中95.3%的人支持强制垃圾分类,但仅21.0%人深入掌握垃圾分类知识;造成成都市垃圾分类失效的原因,73.8%的人认为政策不到位,需要采取强制监管措施,67.6%的人认为垃圾分类桶等基础硬件设施建设不到位,69.2%的人认为居民自身环保意识差,需要加强环保宣传教育。成都市居民生活垃圾治理的WTP金额为12.65元/月/户。年龄、有无住房、户口类型和月收入4个因素对支付额度有显著影响。     

Assessment of groundwater quality by means of self-organizing maps: application in a semiarid area

The Kohonen neural network was applied to hydrochemical data from the Detritic Aquifer of the Lower Andarax, situated in a semiarid zone in the southeast of Spain. An activation map was obtained for each of the sampling points, in which the spatial distribution of the activated neurons indicated different water qualities. To extract the information contained in the activation maps, they were divided into nine quadrats. Cartesian coordinates were assigned to each quadrant (x, y), and for each sampling point, three derived variables were selected, which were assigned the values x and y of the corresponding quadrat. A classification was defined based on this simple matrix system which allows an easy and rapid means of evaluating the water quality. This assessment highlights the various processes that affect groundwater quality. The method generates output that is easier to interpret than from traditional statistical methods. The information is extracted from the activation maps without significant loss of information. The method is proposed for assessing water quality in hydrogeochemically complex areas, where large numbers of observations are made.     

An evaluation of environmental factors affecting species distributions

Many different models can be built to explain the distributions of species. Often there is no single model that is clearly better than the alternatives, and this leads to uncertainty over which environmental factors are limiting species’ distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an area under the curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected model compares to alternative models, and they provide weak inference to accept any selected model.     

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.     

Multivariate curve resolution of multidimensional excitation-emission quenching matrices of a Laurentian soil fulvic acid

Fluorescence excitation–emission matrices (EEM) of aqueous solutions of Laurentian soil fulvic acid (LFA) at three concentrations (50, 75 and 100 mg/l) were obtained at two pH values (pH = 4.0 and 6.0) and as function of the Cu(II) ion concentration. The presence of Cu(II) ion provokes quenching of the intrinsic LFA fluorescence due to complex formation. Multivariate curve resolution (MCR-ALS) was used to successfully decompose single EEM into excitation and emission spectra for the detected components. Moreover, multidimensional (up to six dimensions) data matrices were generated by adding EEM collected as function of the LFA and Cu(II) concentrations and pH. MCR-ALS was able to resolve the excitation and emission spectra from these multidimensional data matrices given further information about the spectral variation profiles induced by the experimental factors. Conditional stability constants (log K_LFACu) were calculated from the quenching profiles observed as function of the Cu(II) concentration, as well as, their trends as function of pH and LFA concentration were obtained – average (and standard deviation) of log K_LFACu = 4.6 ± 0.2. This EEM/MCR-ALS methodology constitutes a new tool for the study of natural organic matter under varying experimental conditions that characterize natural environmental systems.     

2013-2018年北京市PM2.5污染波动特征研究

利用2013—2018年北京市大气污染物监测数据及气象条件等资料,分析了北京市PM_(2.5)污染波动变化趋势及其影响因素。结果表明,2013—2018年北京市空气质量呈现整体改善趋势,优良天数由2013年的176 d增加至2018年的227d;重污染天数显著减少,由2013年的58 d逐年递减至2018年的14 d。受污染源排放、特殊气候现象、气象条件等多因素影响,近6年北京市ρ(PM_(2.5))月均值呈现波动下降趋势,其中秋、冬季波动性更加突显。2014—2016年北京市秋冬季PM_(2.5)污染突出,其中2014年10月、2015年11—12月、2016年12月ρ(PM_(2.5))月均值均达到中度污染级别;而2017—2018年北京市秋、冬季ρ(PM_(2.5))月均值均处于优良水平。相关性分析结果显示,地面相对湿度、中层温度与大气污染物呈现较强的正相关性,中层北风频率、地面风速则呈现负相关性。基于上述气象条件及CO、SO_2和NO_2等气态污染物共同构建的拟合方程对ρ(PM_(2.5))估算结果较好,多站点的拟合值与实际值的相关系数为0.900～0.947,进一步说明气象条件及相关污染源排放对PM_(2.5)污染具有显著影响。     

环境污染区划一种新的多元分析方法

本文应用多元统计分析—ＨｏｔｅｌｉｎｇＴ２检验,对来自客观环境的大气污染实测数据进行了分析,并与常规的ｔ检验、聚类分析方法进行比较,认为该法在环境污染评价问题中,具有较高的分辨环境污染时空分异的效力。