结合 SUＲ 与因子效应原则的多响应质量设计

针对多响应的质量设计问题，本文结合似不相关回归(seemingly unrelated regression， SUＲ) 模型与因子效应原则提出了一种新的建模与优化方法． 该方法不仅结合 SUＲ 模型与因子效应原则筛选出各响应模型的显著性变量，而且运用多变量过程能力指数衡量了过程能力满足规格要求程度的水平． 此外，该方法还通过贝叶斯抽样技术考虑了模型参数不确定性和预测响应值波动对优化结果的影响． 首先，在 SUＲ 模型中针对每个变量设置了一个二元变量指示器以考虑因子效应原则，通过所构建的混合二元变量指示器修正了过程响应和试验因子之间的函数关系; 其次，通过计算混合二元变量指示器和模型结构的后验概率以识别显著性变量，从而确定最佳的模型结构; 然后，在此基础上结合贝叶斯抽样技术构建了一种新的多变量过程能力指数，并通过最大化所构建的多变量过程能力指数获得了最佳的参数设计值; 最后，实际案例研究表明: 本文所提方法不仅能够有效地筛选出多响应过程的显著性变量，而且能够获得最佳的参数设计值．     

半参数计量经济联立模型的变窗宽估计理论

联立方程模型在经济政策制定、经济结构分析和经济预测方面起重要作用.文章将半参数单方程计量经济模型的局部线性估计方法与传统联立方程计量经济模型的工具变量估计方法相结合, 在随机设计(模型中所有变量为随机变量)下, 提出了半参数联立方程计量经济模型的局部线性工具变量变窗宽估计方法, 并利用极限理论研究了估计的大样本性质.结果表明:参数分量的估计具有一致性和渐近正态性且收敛速度为n-1/2;非参数分量估计在内点处具有一致性和渐近正态性, 其收敛速度达到了非参数函数估计的最优收敛速度.     

基于非参数ACE变换的贝叶斯非线性协整检验

针对线性以及非线性协整检验存在模型参数过多、小样本条件下检验功效偏低的问题,提出基于非参数ACE变换的贝叶斯非线性协整VAR模型,运用ACE算法进行变量变换,结合参数的完全条件分布设计Gibbs抽样方案,进行贝叶斯非线性协整检验,并利用Monte Carlo 仿真研究了贝叶斯非线性协整方法的检验势,发现贝叶斯非线性协整...     

非参数计量经济联立模型的变窗宽估计理论

联立方程模型在经济政策制定、经济结构分析和经济预测方面起重要作用. 文章将非参 数回归模型的局部线性估计方法与传统联立方程模型估计方法相结合,在随机设计(模型中所 有变量为随机变量) 下,提出了非参数计量经济联立模型的局部线性工具变量变窗宽估计并利 用概率论中大数定理和中心极限定理等在内点处研究了它的大样本性质,证明了它的一致性 和渐近正态性,它在内点处的收敛速度达到了非参数函数估计的最优收敛速度     

非参数计量经济联立模型的局部线性广义矩估计

联立方程模型在经济政策制定、经济结构分析和经济预测方面起重要作用。本文在随机设计(模型中所有变量为随机变量)下,提出了非参数计量经济联立模型的局部线性广义矩估计并利用概率论中大数定理和中心极限定理在内点处研究了它的大样本性质,证明了它的一致性和渐近正态性。它在内点处的收敛速度达到了非参数函数估计的最优收敛速度。     

基于非参数 ACE变换的贝叶斯非线性协整检验

针对线性以及非线性协整检验存在模型参数过多、小样本条件下检验功效偏低的问题,提出基于非参数 ACE变换的贝叶斯非线性协整 VAR模型, 运用 ACE算法进行变量变换, 结合参数的完全条件分布设计 Gibbs抽样方案, 进行贝叶斯非线性协整检验, 并利用 MonteCarlo仿真研究了贝叶斯非线性协整方法的检验势, 发现贝叶斯非线性协整比经典 Johansen法具有更高更稳健的检验势;同时, 对中国城市和农村居民消费价格指数序列进行实证分析.研究结果表明:贝叶斯非线性协整方法解决了模型中参数过多、小样本条件下检验功效偏低的问题,提高了估计的精确度和检验的准确性     

比例数据基于Tobit分位数回归模型的贝叶斯变量选择

针对家庭商业健康保险参保比例在[0,1]闭区间上取值的特点,本文基于Tobit模型给出了比例响应数据的贝叶斯分位数回归建模方法。通过引入回归系数的“Spike-and-slab”先验分布,应用EM算法我们提出了基于门限规则的贝叶斯变量选择方法。大量数值模拟研究验证了所提的贝叶斯变量选择方法的有效性,且具有易操作、计算量小等优点。最后,将此方法应用到家庭商业健康保险数据的实证分析,研究不同分位数水平下家庭健康保险参保比例的影响因素,得到了许多有意义的研究结果。     

基于贝叶斯因子模型金融高频波动率预测研究

构建了包含时变系数和动态方差的贝叶斯HAR潜在因子模型(DMA(DMS)-FAHAR),并对我国金融期货(主要是股指期货和国债期货)的高频已实现波动率进行预测.通过构建贝叶斯动态潜在因子模型提取包含波动率变量、跳跃变量和考虑杠杆效应的符号跳跃变量等预测变量的重要信息.同时,在模型中加入了投机活动变量,以考察市场投机活动对中国金融期货市场波动率预测的影响.预测结果表明,时变贝叶斯潜在因子模型在所有参与比较的预测模型当中具有最优的短期、中期和长期预测效果.同时,具有时变参数和时变预测变量的贝叶斯HAR族模型在很大程度上提高了固定参数HAR族模型的预测能力.在股指期货和国债期货的预测模型中加入投机活动变量可以获得更好的预测效果.     

套期保值, 估计风险与贝叶斯统计——基于中国铜期货市场的经验研究

针对期货最优套期保值策略估计中可能存在的估计风险问题,本文对单变量线性回归模型(OLS模型)和多变量线性回归模型(VAR模型和EC-VAR模型)进行贝叶斯分析,并采用Gibbs抽样方法对中国铜期货市场的最优套期保值策略进行了实证分析。本文还同时估计了基于频率统计方法的最优套期保值策略,并对贝叶斯统计下和频率统计下的最优套期保值策略进行了分析比较。实证结果清楚表明,估计风险对模型结果有重要影响。在处理估计风险方面,贝叶斯统计较频率统计方法有明显优势。     

随机效应半参数二值响应模型的估计研究北大核心CSSCI

为了研究诸如在收入差距和健康关系中某些解释变量的影响效应可能依赖于其他解释变量的状况,降低可能存在的模型设定和遗漏变量偏误,本文提出了随机效应半参数二值响应模型,其中非参数的设定还可用于数据的初探性分析.随后本文探讨了模型非参数和参数部分的估计.由于似然函数中随机效应和非参数的存在加大了估计难度,为此本文采用B样条方法逼近非参数部分,同时将随机效应视为缺失数据,利用EM算法和MCMC方法建立了模型参数的估计,并研究了其一致性.模拟研究结果表明估计量在有限样本下的表现良好,最后将模型运用于收入差距与健康关系的实例研究中,结果表明数据支持收入差距弱假说.     

A Flexible Count Data Regression Model for Risk Analysis

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.     

Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway‐Maxwell Poisson (COM‐Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM‐Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM‐Poisson GLM, and (2) estimate the prediction accuracy of the COM‐Poisson GLM using simulated data sets. The results of the study indicate that the COM‐Poisson GLM is flexible enough to model under‐, equi‐, and overdispersed data sets with different sample mean values. The results also show that the COM‐Poisson GLM yields accurate parameter estimates. The COM‐Poisson GLM provides a promising and flexible approach for performing count data regression.     

双线性分式交叉规划的等价形式

考虑双线性分式交叉规划,将双线性分式交叉规划转化为线性交叉规划,再借助同参规划组转化为多目标规划,讨论交叉规划的均衡解与多目标规划的最优解的关系。     

A Bayes Linear Bayes Method for Estimation of Correlated Event Rates

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well‐known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates.     

On the Creation of Acceptable Conjoint Analysis Experimental Designs

Conjoint analysis studies typically utilize orthogonal fractional factorial experimental designs to construct a set of hypothetical stimuli. Occasionally, these designs include environmentally correlated attributes that can lead to stimulus profiles that are not representative of the subject's environment. To date, no one has proposed a remedy well-grounded in statistical theory. This note presents a new methodology utilizing combinatorial optimization procedures for creating modified fractional factorial designs that are as “orthogonal” as possible, which do not contain nonrepresentative stimulus profiles.     

Bound and exact methods for assessing link vulnerability in complex networks

Assessing network systems for failures is critical to mitigate the risk and develop proactive responses. In this paper, we investigate devastating consequences of link failures in networks. We propose an exact algorithm and a spectral lower-bound on the minimum number of removed links to incur a significant level of disruption. Our exact solution can identify optimal solutions in both uniform and weighted networks through solving a well-constructed mixed integer program. Also, our spectral lower-bound derives from the Laplacian eigenvalues an estimation on the vulnerability of large networks that are intractable for exact methods. Through experiments on both synthetic and real-world networks, we demonstrate the efficiency of the proposed methods.     

Speaking the Truth in Maritime Risk Assessment

Several major risk studies have been performed in recent years in the maritime transportation domain. These studies have had significant impact on management practices in the industry. The first, the Prince William Sound risk assessment, was reviewed by the National Research Council and found to be promising but incomplete, as the uncertainty in its results was not assessed. The difficulty in assessing this uncertainty is the different techniques that need to be used to model risk in this dynamic and data-scarce application area. In previous articles, we have developed the two pieces of methodology necessary to assess uncertainty in maritime risk assessment, a Bayesian simulation of the occurrence of situations with accident potential and a Bayesian multivariate regression analysis of the relationship between factors describing these situations and expert judgments of accident risk. In this article, we combine the methods to perform a full-scale assessment of risk and uncertainty for two case studies. The first is an assessment of the effects of proposed ferry service expansions in San Francisco Bay. The second is an assessment of risk for the Washington State Ferries, the largest ferry system in the United States.     

Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations

This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three‐axiom‐based analysis partially validates the correctness and rationality of the proposed Bayesian network model.     

Evaluating Lightning-Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain

It is widely accepted that the relationship between lightning wildfire occurrence and its influencing factors vary depending on the spatial scale of analysis, making the development of models at the regional scale advisable. In this study, we analyze the effects of different biophysical variables and lightning characteristics on lightning-caused forest wildfires in Castilla y León region (Central Spain). The presence/absence of at least one lightning-caused fire in any 4 × 4-km grid cell was used as a dependent variable and vegetation type and structure, terrain, climate, and lightning characteristics were used as possible covariates. Five prediction methods were compared: a generalized linear model (GLM), a random forest model (RFM), a generalized additive model (GAM), a GAM that includes a spatial trend function (GAMs) and a spatial autoregressive model (AUREG). A GAMs with just one covariate, apart from longitude and latitude for each observation included as a combined effect, was considered the most appropriate model in terms of both predictive ability and simplicity. According to our results, the probability of a forest being affected by a lightning-caused fire is positively and nonlinearly associated with the percentage of coniferous woodlands in the landscape, suggesting that occurrence is more closely associated with vegetation type than with topography, climate, or lightning characteristics. The selected GAMs is intended to inform the Regional Government of Castilla y León (the fire and fuel agency in the region) regarding identification of areas at greatest risk so it can design long-term forest fuel and fire management strategies.