供应链产品质量检查策略的比较研究

研究由一个供应商和一个零售商组成的供应链,建立存在质量不确定与检查错误下的报童模型,比较分析全检、抽检、分批抽检以及组合策略在分散式与集中式供应链中对零售商订购决策与质量控制效率的影响.结果表明,次品率的增加会降低零售商订购产品的动因,但会增加组合策略下零售商的订购量与利润,同时对集中式供应链中全检策略下的零售商有利;比较策略可知,在分散式供应链中,若次品率较低,则分批抽检策略最优,而随着次品率的提高,组合策略会更优,而在集中式供应链中,全检策略最优;此外零售商质量控制策略的选择受到检查精度、责任成本、次品率阈值、抽检数量以及分批次数等诸多因素的影响,最优策略不尽相同,但不会改变零售商利润随次品率变化的趋势.     

Optimal sampling frequency for volatility forecast models for the Indian stock markets

This paper evaluates the performance of conditional variance models using high‐frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non‐normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd.     

预期短缺ES估计的稳定性分析

在风险管理中,风险量度估计的稳定性对金融机构的经济资本确定及风险分配起着重要的作用.本文从预期短缺(ESα)估计的稳定性角度分析重要性抽样技术和Monte Carlo模拟在估计信用资产组合ESα方面的差异.结果表明,由于组合损失分布尾部事件的稀有性,与传统的Monte Carlo模拟方法相比,运用重要性抽样方法估计的ESα比较稳定,且生成的风险贡献能够明显地体现出资产间不同的风险特征.     

归纳式学习中连续型数据的区间划分问题

主要研究的是归纳式学习中连续型样本数据的分组问题 .在归纳式学习方法中 ,对样本数据的不同分组会直接影响到所生成的反映变量间相互关系的规则 ,从而影响到对新样本的识别效果 .特别是对样本数据为连续型的数据的分组问题 ,目前尚未得到很好的解决 .本文在类相关离散化方法的基础上 ,提出了用极大熵法进行初始区间划分 ,用多因素优选法调整区间的边界 ,二阶概率统计检验与实际意义相结合进行区间合并的一整套划分区间的方法 ,并用本文建立的新方法体系对中国宏观经济的某些指标间的相互关系进行了分析 ,取得了良好的效果.     

基于Kriging模型与重要性抽样的可靠性灵敏度分析

针对因无法获得功能函数的梯度信息而不能使用解析方法的情形,提出了进行可靠性灵敏度分析的高效的仿真方法,首先基于Kriging模型和重要性抽样去计算失效概率,然后通过记分函数(score function)方法求出失效概率对各个参数的偏导数。在计算失效概率时采用反问题(inversion problems)中的不确定性逐步减少(stepwise uncertainty reduction)准则来更新功能函数的Kriging模型,继而在重要性抽样的框架下将失效概率表示成一个"增大"的失效概率与修正项的乘积;而记分函数方法只是对前面抽样方法的一个简单后处理,不需要计算额外的功能函数值.对所提方法使用算例验证表明:当功能函数为昂贵的计算模型或对系统(非单个构件)进行灵敏度分析时,该方法具有较高的计算效率和精度。     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

Non-invasive genetic study and population monitoring of the brown bear (Ursus arctos) (Mammalia: Ursidae) in Kastoria region – Greece

The brown bear (Ursus arctos) in Greece is considered endangered but little is known about the genetic status and the exact size of local populations. Non-invasive genetic sampling was used in this study to investigate the genetic diversity and genetic structure of the brown bear population in the Kastoria region (northwest Macedonia, Greece) and to estimate its population size. Estimation of demographic parameters was based on innovative, well-evaluated methods that can provide estimates from a single sampling session. DNA was extracted from hairs, scat and blood samples and subsequent amplification of 10 microsatellite loci allowed the identification of a minimum number of 75 living bears in the study area while the mark–recapture-based analysis resulted in a point estimation of 219 individuals. Relatively high diversity values, lack of heterozygosity deficiency as well as estimated effective population size, support the Kastoria bear population having good conservation status.     

基于启发式信息熵的粗集数值属性离散化算法

在一致性假设前提下,以数据集的统计性质作为启发式知识,从候选离散点集中选择离散点,根据数据集的期望值和方差来确定搜索最优离散点的区域,提出一种新的基于信息熵粗集数值属性离散化算法,并采用UCI国际标准数据集来验证新算法.新算法与已报道的算法所得到的离散断点集完全一致,决策表的离散化结果也相同,但时间代价不同,新算法比其计算效率提高40%～50%.     

尾矿坝振动实验数据样本容量的优化研究

针对铀尾矿坝振动实验逸出氡累积浓度数据样本容量设计的主观性,本文基于偏向角绝对值均值递减还原逸出氡累积浓度曲线,采用随机起点等距抽样方法,得到不同样本容量的数据子集;通过曲线拟合和样本决定系数度量不同子集对还原实验数据集的代表能力。在保障实验研究精度条件下,得到使实验成本较优的最小样本容量,实验结果表明在样本决定系数大于0.98时,优化后样本容量为实验数据样本容量的10%以下。     

基于非等量采样的伪码同步技术研究

针对数字通信系统中伪随机噪声(pseudo-random noise, PN)码同步技术的同步精度受限于采样间隔的问题,结合可变群时延的高精度同步思想,利用采样点位置偏移量的变化,推导出了非等量采样(non-commensurate sampling, NCS)下的PN码相关函数解析表达式,提出了基于精度因子的NCS率选取准则,通过精度因子可快速判断出NCS后PN码的同步精度下限。在此基础上,分析了码序列周期、接收机前端滤波器等附加因素对PN码同步精度的影响。对NCS率选取准则的研究,为优化数字通信系统设计提供了理论指导,达到以低硬件消耗实现高精度同步的目的。