基于最优支持向量机模型的经营失败预警研究

根据中国资本市场的实际和样本数据特点,设计一套从样本准备到模型参数优化、再到模型比较的集成解决方案,对上市公司经营失败进行预警,通过实验分析参数调整和核函数选择对支持向量机建模的影响,寻求最优的支持向量机模型.实证结果表明,经营失败预警应用中,参数和核函数的选择对预警模型有较大影响,基于最优支持向量机模型的预测效果优于统计方法和神经网络方法,支持向量机适合中国上市公司分行业小样本的实际.特别处理事件作为经营失败样本切分标准对模型产生一定影响.     

联络及其在现代物理中的解释

本文从曲线坐标、曲面上向量平移入手,导入了联络,继而引入协变微分、短程线及曲率张量,最后指明联络在广义相对论中的意义。     

Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.     

基于相似样本归并的大样本混合信用评估模型

当前面向大样本设计的信用评估模型,大多没有深入探究大样本的分布特征,只是简单地将传统评估方法应用在大样本上.首先提出了用于描述大样本分布特征的相关属性集、边界向量等若干概念及定义,并证明了其主要性质.之后在两个大样本数据集的基础上,研究了样本在相似性方面的分布特征,最后设计了一种大样本混合信用评估模型——HLSCE模型.HLSCE模型认为在大样本数据集中,样本的同一属性在不同局部区域内,对分类性能的贡献是不同的.具体地,HLSCE模型根据各样本与边界向量的相似性差异,结合生物启发式算法,将样本归并划分为若干子集并分别在其上训练基分类器.实证研究表明,HLSCE模型的分类精度相比于现有的代表性信用评估模型更高,同时也具有更为优越的平衡性与稳定性.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

基于支持向量机的新经济增长点选择研究

针对现阶段新经济增长点选择模型无法区分“已有的”增长点与“新的”增长点的问题,使用支持向量机挖掘新经济增长点的潜在性.研究显示:陕西省2010年38个工业行业可划分为“新经济增长点”与“非新经济增长点”两类,新经济增长点一类中前十位行业与陕西省“十二五”规划中的文化产业、高新技术产业、新能源产业发展相一致,可见支持向量机在新经济增长点选择中的可行性和可靠性.     

人脸识别中多目标最优不相关图像鉴别分析研究

考虑图像投影鉴别分析问题,为提高特征抽取的速度和识别率,利用图像矩阵直接构造图像散布矩阵,在具有统计不相关的条件下将Foley-Sammon鉴别分析(FSLDA)转化为两目标约束优化问题,并给出了有效投影向量的概念;根据多目标优化的最优性条件可将求取有效投影向量的问题归结为求广义特征方程的最大特征值对应的特征向量,并据此进行特征抽取,进而提出了两目标最优图像投影鉴别分析方法。与其他鉴别投影分析方法相比,该方法具有以下特点:(1)可直接由图像矩阵构建散布矩阵;(2)有效投影向量具有统计不相关性;(3)训练样本的类内散布矩阵不必为可逆的,也不需要求某种形式矩阵的逆。在ORL标准人脸库和NUST603人脸库上的试验结果表明,上述图像投影鉴别分析方法在识别性能上较以往的方法有一定的提高,尤其是特征抽取的速度有明显的提高。     

Normalization in Econometrics

The issue of normalization arises whenever two different values for a vector of unknown parameters imply the identical economic model. A normalization implies not just a rule for selecting which among equivalent points to call the maximum likelihood estimate (MLE), but also governs the topography of the set of points that go into a small-sample confidence interval associated with that MLE. A poor normalization can lead to multimodal distributions, disjoint confidence intervals, and very misleading characterizations of the true statistical uncertainty. This paper introduces an identification principle as a framework upon which a normalization should be imposed, according to which the boundaries of the allowable parameter space should correspond to loci along which the model is locally unidentified. We illustrate these issues with examples taken from mixture models, structural vector autoregressions, and cointegration models.     

On a mixture vector autoregressive model

The authors show how to extend univariate mixture autoregressive models to a multivariate time series context. Similar to the univariate case, the multivariate model consists of a mixture of stationary or nonstationary autoregressive components. The authors give the first and second order stationarity conditions for a multivariate case up to order 2. They also derive the second order stationarity condition for the univariate mixture model up to arbitrary order. They describe an EM algorithm for estimation, as well as a diagnostic checking procedure. They study the performance of their method via simulations and include a real application.     

农产品销量预测的支持向量机方法

运用支持向量机(Support Vector Machine,SVM)智能预测方法对农产品的消费市场需求进行动态预测。为提高农产品销量预测精度,充分考虑了农产品供需随天气变化、气候条件、节假日等因素的影响而动态变化的情况,将这些影响因素纳入农产品销量预测中,运用模糊理论进行模糊化处理;在此基础上提出以支持向量机方法为主、多方法融合为辅的智能预测系统,对农产品销量进行动态预测。实际算例验证了这一智能预测系统的精确性。