加速寿命数据的贝叶斯建模与分析

在加速寿命试验的可靠性设计中, 随机化设计的限制以及删失数据不可避免地导致低分位数估计出现较大的偏差。针对上述的问题, 结合贝叶斯抽样技术以及非线性混合模型(nonlinear mixed model, NLMM)提出了一种可靠性改进的分析方法。首先, 需要检验所收集的数据是否服从威布尔分布以及验证形状参数是否是恒定常数。其次, 考虑随机效应对尺度参数和形状参数的影响, 运用NLMM构建了尺度参数和形状参数与试验因子之间的函数关系。然后, 利用贝叶斯方法估计低分位数的可靠性寿命。最后, 实际案例研究表明, 在考虑删失问题和未完全随机设计的影响时, 所提方法能够获得更为稳健和可靠的估计结果。     

基于DBN的区域计算机联锁系统可靠性分析

区域计算机联锁设备是实现区域内行车安全、保证运输效率的核心设备,对其可靠性研究具有重要意义。结合目前存在的2种区域联锁制式,采用一种新的联锁方案,即在主控站和从控站(选择其中1站或多站)均设置联锁设备。综合考虑联锁系统的共因故障和可维修等因素,利用动态贝叶斯网络对其进行可靠性分析。首先,从系统故障■安全和危险输出的角度出发,建立区域两联锁单元和三联锁单元的动态故障树,并将其转换为相应的动态贝叶斯网络模型;然后利用动态贝叶斯网络的推理特性,对区域联锁系统进行可靠性分析;最后比较了该方法与基于静态贝叶斯网络和动态故障树分析法的结果。计算结果表明:主控站和其中之一的从控站均设置联锁设备是实现区域联锁的较佳方式;且基于动态贝叶斯网络的系统可靠性分析较上述两种方法在计算准确度和时间复杂度方面均有明显优势;并通过动态贝叶斯网络的诊断推理可知,共因故障是系统故障的主要原因,因此应重点防范以降低事故发生的概率。     

Description of the aberrant Leptopilina lasallei n. sp., with an updated phylogeny of Leptopilina Förster (Hymenoptera: Figitidae: Eucoilinae)

ABSTRACT

In the search for native Asian parasitoids of Drosophila suzukii, the notorious spotted-wing Drosophila (SWD), an odd new species of Eucoilinae was discovered. Leptopilina lasallei sp. nov. is herein described and diagnosed relative to other eucoilines associated with drosophilid hosts. Morphologically, L. lasallei is somewhat aberrant within Leptopilina; phylogenetically, L. lasallei is sister group to the core Leptopilina. In the process of investigating L. lasallei, a de novo molecular phylogeny of Leptopilina was generated and is included here. The integrated approach used for the characterisation of L. lasallei, and the resulting phylogeny of Leptopilina, produced data useful to select parasitoid species for SWD biological control.http://www.zoobank.org/urn:lsid:zoobank.org:act:402D504A-4616-4524-85D7-1C13A6276F06 http://www.zoobank.org/urn:lsid:zoobank.org:act:402D504A-4616-4524-85D7-1C13A6276F06     

独立学院应用统计学专业实践教学模式改革

依据独立学院的人才培养目标,分析了独立学院应用统计学专业实践教学现状,论述了独立学院应用统计学专业实践教学模式改革的思路,构建了独立学院应用统计学专业实践教学体系,探讨了独立学院应用统计学专业实践课程的教学,力求为独立学院应用统计学和其他相关专业特色化实践教学模式的建立提供一定的参考.     

典型常减压装置塔类设备腐蚀失效案例统计分析

通过对多套典型常减压装置塔类设备的腐蚀失效案例进行归纳总结，统计发生严重腐蚀的塔类设备的腐蚀失效类型、重点腐蚀部位、材质应用类型的数量和比例，得出腐蚀问题发生最多的是碳钢材质的塔类设备，易腐蚀部位一般为塔壁和焊缝，且塔顶腐蚀情况较其他部位严重，此外坑蚀现象是出现最多的腐蚀失效类型。进而结合原油性质和加工特点，分析其对典型的5套常减压装置塔类设备的腐蚀影响，最后对塔类设备典型腐蚀问题进行了腐蚀原因分析，并给出相应的选材和防腐建议。     

基于时间窗的SVDBN近似推理算法研究

变结构动态贝叶斯网络(SVDBN)在处理非稳态过程的不确定问题具有其独特的优越性。为克服SVDBN推理算法不能实现在线推理的缺陷,在引入SVDBN的时间窗和时间窗宽度概念基础上,阐述了基于时间窗的SVDBN在线近似推理机制,提出了2种在线近似推理算法,即基于时间窗的变结构离散动态贝叶斯网络(SVDDBN)递推推理算法和基于时间窗的SVDDBN快速推理算法。通过仿真实验验证了这2种算法的有效性,并从复杂度、适用范围及更新时间等方面进行了比较。     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

边坡可靠度的Bayes估计及后验稳健性

采用边坡自身的稳定性参数构建似然函数，以研究区内21个极限状态坡的稳定性参数为样本，建立先验分布，应用Bayes理论确定后验分布，应用后验稳健性分析方法对西安咸阳国际机场污水排放口泾阳黄土边坡进行稳定性评价。结果表明:后验分布的均值得到了很好的修正，方差小，精度高；当稳定性评价指标先验分布存在5%、10%和15%波动时，后验期望损失依次增大，但均在20%范围内波动，这表明边坡可靠度的Bayes估计法具有很好的稳健性，可对边坡的稳定性进行更为精确地评价。     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.