A Modular Bayesian Salmonella Source Attribution Model for Sparse Data

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source‐specific effects and the salmonella subtype‐specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.     

Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.     

Using Adaptive Sparse Grids to Solve High‐Dimensional Dynamic Models

We present a flexible and scalable method for computing global solutions of high‐dimensional stochastic dynamic models. Within a time iteration or value function iteration setup, we interpolate functions using an adaptive sparse grid algorithm. With increasing dimensions, sparse grids grow much more slowly than standard tensor product grids. Moreover, adaptivity adds a second layer of sparsity, as grid points are added only where they are most needed, for instance, in regions with steep gradients or at nondifferentiabilities. To further speed up the solution process, our implementation is fully hybrid parallel, combining distributed and shared memory parallelization paradigms, and thus permits an efficient use of high‐performance computing architectures. To demonstrate the broad applicability of our method, we solve two very different types of dynamic models: first, high‐dimensional international real business cycle models with capital adjustment costs and irreversible investment; second, multiproduct menu‐cost models with temporary sales and economies of scope in price setting.     

Staffing and scheduling flexible call centers by two-stage robust optimization

We study the shift scheduling problem in a multi-shift, flexible call center. Differently from previous approaches, the staffing levels ensuring the desired quality of service are considered uncertain, leading to a two-stage robust integer program with right-hand-side uncertainty. We show that, in our setting, modeling the correlation of the demands in consecutive time slots is easier than in other staffing approaches. The complexity issues of a Benders type reformulation are investigated and a branch-and-cut algorithm is devised. The approach can efficiently solve real-world problems from an Italian call center and effectively support managers decisions. In fact, we show that robust shifts have very similar costs to those evaluated by the traditional (deterministic) method while ensuring a higher level of protection against uncertainty.     

副省级城市综合韧性评价及障碍因子识别

为了提升副省级城市韧性,探索其可持续发展路径。该文以15个副省级城市为研究对象,运用改进的CRITIC-熵权组合赋权法、TOPSIS法、方差分解法、障碍度模型对2011—2020年15个副省级城市综合韧性进行评价,并对其障碍因子进行识别分析。结果显示:15个副省级城市综合韧性在2011—2020年间呈现波动上升的趋势,整体形势较好;各副省级城市综合韧性、分项韧性排名存在明显差异;从方差分解结果来看,宁波、成都、长春、哈尔滨四个城市的经济韧性和生态韧性对综合韧性的贡献度最大,而其余副省级城市则是经济韧性和社会韧性的贡献度最大;从障碍度结果分析,经济系统和社会系统是制约副省级城市韧性提升的主要障碍系统,对于具体指标,每百人公共图书馆藏书(b₃)和一般公共预算收入(a₄)是大部分副省级城市的主要障碍因子。未来,各副省级城市应依据发展情况着力补齐短板,从完善社会应急保障体系、扩大社会文化产业影响力和增加科教领域的投入等方面增强城市韧性,推动副省级城市实力整体提升。     

Equilibrium Wage Dispersion with Worker and Employer Heterogeneity

We construct and estimate an equilibrium search model with on–the–job–search. Firms make take–it–or–leave–it wage offers to workers conditional on their characteristics and they can respond to the outside job offers received by their employees. Unobserved worker productive heterogeneity is introduced in the form of cross–worker differences in a “competence” parameter. On the other side of the market, firms also are heterogeneous with respect to their marginal productivity of labor. The model delivers a theory of steady–state wage dispersion driven by heterogenous worker abilities and firm productivities, as well as by matching frictions. The structural model is estimated using matched employer and employee French panel data. The exogenous distributions of worker and firm heterogeneity components are nonparametrically estimated. We use this structural estimation to provide a decomposition of cross–employee wage variance. We find that the share of the cross–sectional wage variance that is explained by person effects varies across skill groups. Specifically, this share lies close to 40% for high–skilled white collars, and quickly decreases to 0% as the observed skill level decreases. The contribution of market imperfections to wage dispersion is typically around 50%.     

惯性灰色模型及其应用性质研究

研究灰色预测模型建模的演化过程，可以更好地了解模型的本质特征和状态变化。惯性灰色模型主要研究灰色预测模型建模的演化过程，了解系统变化状态。本文根据数据的力学特性，利用矩阵分析方法研究惯性灰色模型的建模步骤，简化文献［1］中惯性模型的结构参数和分量参数形式，总结求解各种数据序列的力学变换式，获取各种惯性灰色模型的建模机理。最后通过实例研究系统状态的演变过程，将惯性灰色GM(1,1)模型应用到交通流状态的判定中，得到三相交通流与三种惯性灰色GM(1,1)模型的对应关系。利用三种惯性模型模拟效果来准确判断交通流的状态，揭示交通系统实时特性，为交通规划、控制和优化提供可靠的理论依据。     

中立型两阶段交叉效率评价方法

在数据包络分析中，已有的两阶段交叉效率评价方法，不仅只能用于基本两阶段网络结构，而且没有中立地分解子阶段效率。文章提出了一个既适用于基本两阶段网络结构，又适用于具有共享输入的两阶段网络结构的，中立型交叉效率评价方法。该方法定义自评时整体效率等于子阶段效率的加权和，在自评整体效率最大的前提下，从使各子阶段效率都尽可能大的角度为每个决策单元分别确定一组最优权重，进而通过互评计算决策单元整体和子阶段的最终效率得分。最后，通过两个实例验证了方法的实用、合理、有效。     

Selection of linear mixed-effects models for clustered data

We consider model selection for linear mixed-effects models with clustered structure, where conditional Kullback–Leibler (CKL) loss is applied to measure the efficiency of the selection. We estimate the CKL loss by substituting the empirical best linear unbiased predictors (EBLUPs) into random effects with model parameters estimated by maximum likelihood. Although the BLUP approach is commonly used in predicting random effects and future observations, selecting random effects to achieve asymptotic loss efficiency concerning CKL loss is challenging and has not been well studied. In this paper, we propose addressing this difficulty using a conditional generalized information criterion (CGIC) with two tuning parameters. We further consider a challenging but practically relevant situation where the number, $m$, of clusters does not go to infinity with the sample size. Hence the random-effects variances are not consistently estimable. We show that via a novel decomposition of the CKL risk, the CGIC achieves consistency and asymptotic loss efficiency, whether $m$ is fixed or increases to infinity with the sample size. We also conduct numerical experiments to illustrate the theoretical findings.     

Robust optimal estimation of location from discretely sampled functional data

Estimating location is a central problem in functional data analysis, yet most current estimation procedures either unrealistically assume completely observed trajectories or lack robustness with respect to the many kinds of anomalies one can encounter in the functional setting. To remedy these deficiencies we introduce the first class of optimal robust location estimators based on discretely sampled functional data. The proposed method is based on M-type smoothing spline estimation with repeated measurements and is suitable for both commonly and independently observed trajectories that are subject to measurement error. We show that under suitable assumptions the proposed family of estimators is minimax rate optimal both for commonly and independently observed trajectories and we illustrate its highly competitive performance and practical usefulness in a Monte-Carlo study and a real-data example involving recent Covid-19 data.