Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures

During the last years, graphical models have become a popular tool to represent dependencies among variables in many scientific areas. Typically, the objective is to discover dependence relationships that can be represented through a directed acyclic graph (DAG). The set of all conditional independencies encoded by a DAG determines its Markov property. In general, DAGs encoding the same conditional independencies are not distinguishable from observational data and can be collected into equivalence classes, each one represented by a chain graph called essential graph (EG). However, both the DAG and EG space grow super exponentially in the number of variables, and so, graph structural learning requires the adoption of Markov chain Monte Carlo (MCMC) techniques. In this paper, we review some recent results on Bayesian model selection of Gaussian DAG models under a unified framework. These results are based on closed-form expressions for the marginal likelihood of a DAG and EG structure, which is obtained from a few suitable assumptions on the prior for model parameters. We then introduce a general MCMC scheme that can be adopted both for model selection of DAGs and EGs together with a couple of applications on real data sets.     

The mixture index of fit and minimax regression

A measure of the fit of a statistical model can be obtained by estimating the relative size of the largest fraction of the population where a distribution belonging to the model may be valid. This is the mixture index of fit that was suggested for models for contingency tables by Rudas, Clogg, Lindsay (1994) and it is extended here for models involving continuous observations. In particular, the approach is applied to regression models with normal and uniform error structures. Best fit, as measured by the mixture index of fit, is obtained with minimax estimation of the regression parameters. Therefore, whenever minimax estimation is used for these problems, the mixture index of fit provides a natural approach for measuring model fit and for variable selection. Received: September 1997     

我国制造业响应型供应链模型的构建与研究——SCOR模型的本土化

本文以SCOR模型为参照,构建了以制造业为领导核心的响应型供应链模型,并对其进行深入研究,在此基础上给出供应链绩效衡量的关键指标体系及其计算方法.响应型供应链模型针对我国制造业在引进国外先进供应链管理模式过程中的＂水土不服＂现象,从快速响应客户角度探索适合我国制造业的供应链运作模式,以其学术界和企业界从更多角度探索适合我国国情的供应链管理新模式.     

军工企业供应链敏捷化信息集成模式的建模分析

从供应链信息流结构的角度,研究供应链的敏捷化信息流管理,用图论中的标号图构建供应链的信息流结构模型,给出信息集成图、信息集成块和信息集成度的概念,并在文献研究基础上作了一定的扩展,提出了一种新的军工企业信息集成模式,其主要目标是通过集成块内的信息中心使其军工企业的信息不仅可以任意交流,而且集成块间也可以通过一定的协调实现信息完全共享。     

Multivariate Clustered Data Analysis in Developmental Toxicity Studies

In this paper we review statistical methods for analyzing developmental toxicity data. Such data raise a number of challenges. Models that try to accommodate the complex data generating mechanism of a developmental toxicity study, should take into account the litter effect and the number of viable fetuses, malformation indicators, weight and clustering, as a function of exposure. Further, the size of the litter may be related to outcomes among live fetuses. Scientific interest may be in inference about the dose effect, on implications of model misspecification, on assessment of model fit, and on the calculation of derived quantities such as safe limits, etc. We describe the relative merits of conditional, marginal and random-effects models for multivariate clustered binary data and present joint models for both continuous and discrete data.     

Option Pricing under the Double Exponential Jump-Diffusion Model with Stochastic Volatility and Interest Rate

This paper proposes an efficient option pricing model that incorporates stochastic interest rate (SIR), stochastic volatility (SV), and double exponential jump into the jump-diffusion settings. The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns, rare events, and an SIR. Using the model, we deduce the pricing characteristic function and pricing formula of a European option. Then, we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV. For verification purposes, we conduct time efficiency analysis, goodness of fit analysis, and jump/drift term analysis of the proposed model. In addition, we compare the pricing accuracy of the proposed model with those of the Black–Scholes and the Kou (2002) models. The empirical results show that the proposed option pricing model has high time efficiency, and the goodness of fit and pricing accuracy are significantly higher than those of the other two models.     

Information visualisation based on graph models

Information visualisation is a key component of support tools for many applications in science and engineering. A graph is an abstract structure that is widely used to model information for its visualisation. In this paper, we consider practical and general graph formalism called hierarchical graphs and present the Higres and Visual Graph systems aimed at supporting information visualisation on the base of hierarchical graph models.     

不同模型拟合优度下的多响应优化方法

响应曲面方法是对产品质量改进的一种有效的试验设计方法。目前,在响应模型的不同拟合优度问题基础上的多响应优化方法还有待进一步的研究。满意度函数方法是一种简单易行的响应曲面方法,而传统的满意度函数方法并未涉及响应模型的拟合优度。文章在指数满意度函数基础上提出了一种改进的满意度函数方法,以解决试验设计中不同拟合优度下的多响应优化问题。该方法将调整的Ra2dj作为响应曲面模型拟合优度的衡量标准,考虑了响应模型的拟合优度因素,分析了不同的拟合优度对多响应优化结果的影响。最后,通过实例对该方法进行验证,得到了较好的效果。     

Investigating fit in supply chain integration: A systematic literature review on context,practices, performance links

This paper offers a systematic literature review on the fit among context, supply chain integration (SCI) and performance based on the analysis of 116 articles published in 28 peer-reviewed journals. By using Venkatraman's (1989) model, which distinguishes fit into different forms, to frame previous literature, and Hakansson's (1982) interaction model to classify the investigated contextual variables, the present research offers an original lens through which to examine the results found and to identify some open and unsolved issues and opportunities for future research. Based on these results, the research concludes that the most used forms of fit are mediation and moderation. Some examples of popular research topics include the moderating role of uncertainty/complexity in influencing SCI benefits and the role of SCI as a prerequisite for other operations and supply chain management practices. In addition, future research opportunities exist in several areas, including (1) the adoption of a behavioural operations supply chain management perspective and institutional theory to study SCI antecedents, and (2) the study of national culture as a moderator of the link between SCI and performance. However, the most promising research opportunities come from the less used fit forms (e.g. profile deviation and fit as matching) and from combinations of multiple fit forms that could help to address some unsolved issues in SCI, such as the balance between upstream and downstream integration and optimal SCI profiles. The arguments discussed could be useful for both academics and practitioners interested in the SCI-performance link and the role of context.     

Bayesian Graphical Models for STructural Vector Autoregressive Processes

This paper proposes a Bayesian, graph‐based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm to estimate jointly the two causal structures and the parameters of the reduced‐form VAR model. The BGVAR approach is shown to be quite effective in dealing with model identification and selection in multivariate time series of moderate dimension, as those considered in the economic literature. In the macroeconomic application the BGVAR identifies the relevant structural relationships among 20 US economic variables, thus providing a useful tool for policy analysis. The financial application contributes to the recent econometric literature on financial interconnectedness. The BGVAR approach provides evidence of a strong unidirectional linkage from financial to non‐financial super‐sectors during the 2007–2009 financial crisis and a strong bidirectional linkage between the two sectors during the 2010–2013 European sovereign debt crisis. Copyright © 2015 John Wiley & Sons, Ltd.     

Time series estimation of the bond default risk premium

The bond default risk premium, measured by the spread between higher and lower grade bond returns, is often estimated with univariate time series procedures and used as an input in financial models. In this paper, time series properties of the historical default risk premium are analyzed and forecasting results from univariate time series models are compared. An autoregressive model with an overreaction component provides the best statistical fit for the bond default risk premium series. A random walk model exhibits the worst fit. The findings are robust over a variety of model specifications and measurement choices. For all forms of the time series process the univariate time series models explain a small percentage of the variation in the default risk premium, raising questions about traditional approaches to estimating the expected default risk premium.     

国际碳交易市场的风险度量及对我国的启示——基于状态转移与极值理论的VaR比较研究

本文以欧洲气候交易所公布的CERs期货报价为研究对象,将Markov波动转移引入VaR的计算,结合极值理论,度量国际碳交易市场的系统风险。首先建立SWARCH模型与MS-GARCH模型描述价格波动的阶跃特性,直接测算动态VaR。随后采用POT模型拟合标准残差序列的右尾超门限分布,确定极值分位数,再次测算动态VaR。最后通过回测检验选取最优风险值,并由此分析了我国获批碳项目变动趋势与国际碳交易市场风险变动趋势间的关系。     

Monotone dependence in graphical models for multivariate Markov chains

We show that a deeper insight into the relations among marginal processes of a multivariate Markov chain can be gained by testing hypotheses of Granger noncausality, contemporaneous independence and monotone dependence. Granger noncausality and contemporaneous independence conditions are read off a mixed graph, and the dependence of an univariate component of the chain on its parents—according to the graph terminology—is described in terms of stochastic dominance criteria. The examined hypotheses are proven to be equivalent to equality and inequality constraints on some parameters of a multivariate logistic model for the transition probabilities. The introduced hypotheses are tested on real categorical time series.     

关于R²的几点质疑

本文论证线性回归分析指标R2拟合意义的局限性,并质疑R2检验的正当性。通过经济计量学课程实验资料揭示R2的局限,实证分析经济计量学软件Eviews R2计算公式的误区。此外,本文提出并建议采用残差误差率指标作为回归分析拟合优度的可决系数较为适宜。     

Hierarchical models for independence structures of networks

We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random ties) of the network. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the dyads of the network, called the dependency graph. Every network model with dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erdös–Rényi and the β models to create hierarchical Erdös–Rényi and β models. We describe various methods for parameter estimation, as well as simulation studies for models with sparse dependency graphs.     

基于供应链管理的两阶段联合库存S.D模型

运用系统动力学方法构建了基于供应链管理的联合库存S.D模型。首先,在联合库存系统边界优化的基础上,构建了基于供应链管理的两阶段联合库存因果关系图;其次,构造出S.D模型的流图,确切描述出反馈系统的动态性能,界定了流图的主要变量,构造合理的模拟方程和函数,阐述流图中各子模块内部运行机理;最后,结合具体公司的库存管理现状,在模型通过检验的前提下,进行了模型行为预测分析及联合库存模式与传统库存模式之对比分析,从中得出一些较有实际意义的模拟分析结果,为优化库存提供了有益的理论指导。     

On the inclusion of bivariate marked point processes in graphical models

Graphical models use graphs to represent conditional independence relationships among random variables of a multivariate probability distribution. This paper introduces a new kind of chain graph models in which nodes also represent marked point processes. This is relevant to the analysis of event history data, i.e. data consisting of random sequences of events or time durations of states. Survival analysis and duration models are particular cases. This article considers the case of two marked point processes. The idea consists of representing a whole process by a single node and a conditional independence statement by a lack of connection. We refer to the resulting models as graphical duration models.     

Testing structural equation models: the impact of error variances in the data generating process

Yet another paper on fit measures? To our knowledge, very few papers discuss how fit measures are affected by error variance in the Data Generating Process (DGP). The present paper deals with this. Based upon an extensive simulation study, this paper shows that the effects of increased error variance differ significantly for various fit measures. In addition to error variance the effects depend on sample size and severity of misspecification. The findings confirm the general notion that good fit as measured by the chi-square, RMSEA and GFI etc. does not necessarily mean that the model is correctly specified and reliable. One finding is that the chi square test may give support to misspecified models in situations with a high level of error variance in the DGP, for small sample sizes. Another finding is that the chi-square test looses power also for large sample sizes when the model is negligible misspecified. Other results include incremental fit indices as NFI and RFI which prove to be more informative indicators under these circumstances. At the end of the paper we formulate some guidelines for use of different fit measures.     

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.