基于灰色理论的危险品运输风险研究

针对目前海上危险品运输安全现状,根据系统工程原理和层次分析方法,研究并提出了海上危险品运输安全风险评价指标体系;运用灰色系统理论和模糊数学设计了具体的海上危险品运输安全风险评价模型;通过实例验证,表明该评价方法能够比较科学、准确地综合评价海上危险品运输安全状况,具有可行性及可操作性特点。     

基于灰色理论的危险品运输风险研究

针对目前海上危险品运输安全现状,根据系统工程原理和层次分析方法,研究并提出了海上危险品运输安全风险评价指标体系;运用灰色系统理论和模糊数学设计了具体的海上危险品运输安全风险评价模型;通过实例验证,表明该评价方法能够比较科学、准确地综合评价海上危险品运输安全状况,具有可行性及可操作性特点.     

基于RFID技术的危险品物流监控系统模型研究

结合危险品特性及我国当前危险品物流现状,提出基于RFID、GPS、CDMA、GIS和现场总线技术,对危险品存储和运输物流过程进行自动信息采集、实时跟踪的危险品物流监控系统模型,详细阐述了模型架构和各子系统设计,并分析了模型具有的优势。该模型可提高危险品物流管理的效率和安全性,具有一定的市场应用前景。     

城市道路危险品运输限速与监控系统设计

文章对危险品运输限速模型进行了研究,在综合利用专家咨询法和模糊层次分析法的基础上,建立了影响车速的评价指标体系、确定了综合评价等级和相应的修正系数,进而得到危险品运输的安全车速限值,并进行了监控预警系统设计。运行结果表明,文章的研究思路可行,开发出的系统能够准确地监控危险品运输车辆运行状态,为城市道路危险品运输监管系统的构建研究工作提供了一种新的方法和思路。     

航空公司危险品运输应急能力评估模型研究

近年来经济的高速发展,也带动了物流产业的发展。国内外,各行业各部门之间产品的周转速度也越来越快。其中以危险品(包括锂电池、一些化学物品、医疗用品等)作为运输对象的航空运输也愈发频繁。由于运输的对象具有特殊性,一旦发生事故后果不堪设想,为了提高航空公司危险品运输应急处置能力,文章基于几家航空公司的应急手册,以及《中国民用航空危险品运输管理规定》,《AC-276-TR-2018-01危险品货物航空运输存储管理办法》等规章文件以及相关文献和专家的意见,依据航空公司应急管理工作流程和各流程中的质量要求建立一个基于4R模型的危险品运输应急能力评价指标,采用G1序关系法和模糊综合评价法建立航空公司危险品运输应急处置能力评估模型。希望能为航空公司危险品运输事故的应急处置起到抛砖引玉的作用。     

城市道路危险品运输限速与监控系统设计

文章对危险品运输限速模型进行了研究，在综合利用专家咨询法和模糊层次分析法的基础上，建立了影响车速的评价指标体系、确定了综合评价等级和相应的修正系数，进而得到危险品运输的安全车速限值，并进行了监控预警系统设计。运行结果表明，文章的研究思路可行，开发出的系统能够准确地监控危险品运输车辆运行状态，为城市道路危险品运输监管系统的构建研究工作提供了一种新的方法和思路。     

基于系统动力学的道路危险品运输管理政策模拟探讨

伴随着经济的发展,我国石油和化工产业发展迅速,导致道路危险品运输管理工作越来越复杂,加强道路危险品运输管理工作是行业建设者的当务之急。笔者结合多年工作经验,从道路危险品运输管理现状和系统动力学模型着手,对于系统动力学的道路危险品运输管理政策模拟作了简要分析。     

危险品物流事故协同应急救援决策研究

由于危险品本身的特性,在危险品运输过程中一旦发生事故,往往事故后果很严重,危害范围大,需要区域协同起来应对危险品物流应急救援。综合研究了对危险品进行区域协同应急救援指挥体系,建立了协同的应急救援的组织指挥机制。在此基础上建立了危险品应急救援应急响应机制及决策系统。     

基于系统动力学的道路危险品运输管理政策模拟探讨

伴随着经济的发展，我国石油和化工产业发展迅速，导致道路危险品运输管理工作越来越复杂，加强道路危险品运输管理工作是行业建设者的当务之急。笔者结合多年工作经验，从道路危险品运输管理现状和系统动力学模型着手，对于系统动力学的道路危险品运输管理政策模拟作了简要分析。     

一个实时的危险品运输公路网络模型

通过对一个加拿大危险品运输公路网络模型的优缺点的简单分析,修正了三个关键参数,并增加了时间因素,建立了一个适应中国实际国情的实时危险品公路运输网络模型,对模型的应用前景进行了展望。     

The Cox,Ross and Rubinstein tree model which includes counterparty credit risk and funding costs

The binomial asset pricing model of Cox, Ross and Rubinstein (CRR) is extensively used for the valuation of options. The CRR model is a discrete analog of the Black–Scholes–Merton (BSM) model. The 2008 credit crisis exposed the shortcomings of the oversimplified assumptions of the BSM model. Burgard and Kjaer extended the BSM model to include adjustments such as a credit value adjustment (CVA), a debit value adjustment (DVA) and a funding value adjustment (FVA). The aim of this paper is to extend the CRR model to include CVA, DVA and FVA and to prove that this extended CRR model coincides with the model that results from discretising the Burgard and Kjaer model. Our results are numerically implemented and we also show that as the number of time-steps increase in the derived tree structure model, the model converges to the model developed by Burgard and Kjaer.     

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

The analysis of sports data, in particular football match outcomes, has always produced an immense interest among the statisticians. In this paper, we adopt the generalized Poisson difference distribution (GPDD) to model the goal difference of football matches. We discuss the advantages of the proposed model over the Poisson difference (PD) model, which was also used for the same purpose. The GPDD model, like the PD model, is based on the goal difference in each game that allows us to account for the correlation without explicitly modelling it. The main advantage of the GPDD model is its flexibility in the tails by considering shorter as well as longer tails than the PD distribution. We carry out the analysis in a Bayesian framework in order to incorporate external information, such as historical knowledge or data, through the prior distributions. We model both the mean and the variance of the goal difference and show that such a model performs considerably better than a model with a fixed variance. Finally, the proposed model is fitted to the 2012–2013 Italian Serie A football data, and various model diagnostics are carried out to evaluate the performance of the model.     

Queensland State Impact and Projection Model: The Household Sector

The Queensland Impact and Projection model is an integrated input–output econometric model of the Queensland economy. Its purpose is to complement a conventional input–output model for analyzing economic impacts at the state level. This paper provides an overview of some of the methods used to model the household sector in an input–output framework, before describing the approach taken in the Queensland model. Some results which support the empirical performance of the model are also provided. It is demonstrated that the integrated model is a viable alternative and improvement on the conventional input–output model. The results are consistent with the static input–output model and conform to expectations about how the economy responds in real impact situations.     

Forecasting returns in the VIX futures market

This paper introduces a new forecasting model for VIX futures returns. The model is structural in nature and parsimonious, and contains parameters that are relatively easy to estimate. The forecasts of next day VIX futures returns based on this model are superior to those produced by a linear forecasting model that uses the same set of predictors. Moreover, the profits to a market-timing model based on the proposed forecasts are statistically and economically significant, and are robust to both the method used for adjusting for risk and transaction costs (up to around 15 basis points). In contrast, the forecasts generated by the linear forecasting model are not.     

Regression density estimation using smooth adaptive Gaussian mixtures

We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends the existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when the number of covariates is large. Furthermore, we typically need fewer components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self-adjusting mechanism that prevents overfitting and makes it feasible to fit flexible high-dimensional surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulated and real examples are used to show that the full generality of our model is required to fit a large class of densities, but also that special cases of the general model are interesting models for economic data.     

Nowcasting German GDP: Foreign factors,financial markets,and model averaging

This paper develops a nowcasting model for the German economy. The model outperforms a number of alternatives and produces forecasts not only for GDP but also for other key variables. We show that the inclusion of a foreign factor improves the model’s performance, while financial variables do not. Additionally, a comprehensive model averaging exercise reveals that factor extraction in a single model delivers slightly better results than averaging across models. Finally, we estimate a “news” index for the German economy in order to assess the overall performance of the model beyond forecast errors in GDP. The index is constructed as a weighted average of the nowcast errors related to each variable included in the model.     

Closing the Canadian Input–Output Model: Homogeneous vs Non-homogeneous Household Sector Specifications

This paper addresses, on both theoretical and empirical fronts, the task of endogenizing the household sector in a rectangular input–output (IO) model. The formulation of Miyazawa (1976) for square models is extended to the development of an impact matrix for rectangular models. This allows for the numerous household revenue sources to be built into the model. A closed model with non-homogeneous households is developed where individuals are modelled individually and by industry. The perform ance of the non-homogeneous household sector model is compared with a model that has a homogeneous household sector. The model with the non-homogeneous household sector is more sensitive to changes in wages and salaries than to changes in final demand.     

基于灰色神经网络组合模型的废旧产品回收预测

制造企业对废旧产品进行回收再利用,是节约资源和保护环境的有效方式。由于在产品回收过程中存在诸多不确定性因素,如何对产品回收量进行有效预测是亟待解决的关键问题。本文针对废旧产品回收的特点,将灰色GM（1,1）模型与BP神经网络预测方法结合,构建了灰色神经网络组合预测模型对产品回收量进行预测。该组合模型兼具两种预测方法的优点,预测效果优于各独立模型。案例分析表明了该种预测方法的有效性。     

Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts

We propose a family of regression models to adjust for nonrandom dropouts in the analysis of longitudinal outcomes with fully observed covariates. The approach conceptually focuses on generalized linear models with random effects. A novel formulation of a shared random effects model is presented and shown to provide a dropout selection parameter with a meaningful interpretation. The proposed semiparametric and parametric models are made part of a sensitivity analysis to delineate the range of inferences consistent with observed data. Concerns about model identifiability are addressed by fixing some model parameters to construct functional estimators that are used as the basis of a global sensitivity test for parameter contrasts. Our simulation studies demonstrate a large reduction of bias for the semiparametric model relatively to the parametric model at times where the dropout rate is high or the dropout model is misspecified. The methodology's practical utility is illustrated in a data analysis.     

A generalized non-linear forecasting model for limited overs international cricket

This paper proposes a generalized non-linear forecasting model (GNLM) for forecasting the number of runs remaining to be scored in an innings of cricket. The proposed model takes into account the numbers of overs left and wickets lost. The GNLFM can be used to build a model for any format of limited-overs international cricket. However, the purpose of its use in this paper is for building a forecasting model for projecting second innings total runs in Twenty-20 International cricket. Our model makes it possible to estimate the runs differential of the two competing teams whilst the match is in progress. The runs differential can be used not only to gauge the closeness of a game, but also to estimate the ratings of cricket teams that take into account the margin of victory. Furthermore, the well-known original Duckworth/Lewis (DL) model and the McHale/Asif version of it for revising targets in interrupted matches are special cases of our proposed generalized non-linear forecasting model.