考虑异质性行为的灾后应急物资动态调度优化

在以人道主义为本的应急救援活动中，不可忽视被救灾民和施救决策者们异质性行为的影响。一方面，将各灾民因救援物资需求未被及时满足而呈现的差异化心理痛苦度量成经济损失，并纳入应急救援调度的社会成本这一决策目标中；另一方面，在灾后物资调度决策中，关注具有异质性偏好的各应急决策者所展现出的不同救援态度。结合面向联合机会约束规划的动态供需平衡限制，最终构建一个考虑灾民和决策者们异质性行为的多阶段灾后救援物资分配和应急路径优化模型。采用2008年汶川地震为案例背景，应用遗传算法对模型求解和参数分析，将仿真结果与不考虑异质性行为的传统救援调度方案进行比较，得出一些结论为构建高效的应急救援体系提供有益参考。     

Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil

Motivated by a proposal of the local authority for improving the existing healthcare system in the Parana State in Brazil, this article presents an optimization-based model for developing a better system for patients by aggregating various health services offered in the municipalities of Parana into some microregions. The problem is formulated as a multi-objective partitioning of the nodes of an undirected graph (or network) with the municipalities as the nodes and the roads connecting them as the edges of the graph. Maximizing the population homogeneity in the microregions, maximizing the variety of medical procedures offered in the microregions, and minimizing the inter-microregion distances to be traveled by patients are considered as three objective functions of the problem. An integer-coded multi-objective genetic algorithm is adopted as the optimization tool, which yields a significant improvement to the existing healthcare system map of the Parana State. The results obtained may have a strong impact on the healthcare system management in Parana. The model proposed here could be a useful tool to aid the decision-making in health management, as well as for better organization of any healthcare system, including those of other Brazilian States.     

Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model

Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes likelihood analysis of these models computationally infeasible. This article outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.     

Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering. However, this approach is sensitive to both diverse non-normalities of marginal variables and outliers, which are commonly observed in multivariate experiments. We propose mixtures of Gaussian copula factor analyzers (MGCFA) for clustering high-dimensional clustering. This model has two advantages; (1) it allows different marginal distributions to facilitate fitting flexibility of the mixture model, (2) it can avoid the curse of dimensionality by embedding the factor-analytic structure in the component-correlation matrices of the mixture distribution.An EM algorithm is developed for the fitting of MGCFA. The proposed method is free of the curse of dimensionality and allows any parametric marginal distribution which fits best to the data. It is applied to both synthetic data and a microarray gene expression data for clustering and shows its better performance over several existing methods.     

基于协同效应的知识创新团队伙伴选择方法

在知识创新团队的伙伴选择问题中着重考虑了伙伴间的协同效应信息。首先,分析了伙伴之间的协同关系与协同效应,描述了考虑多个协同效应评价指标的知识创新团队伙伴选择问题;然后,建立了团队伙伴选择的数学模型,该模型是一个0-1二次整数规划问题,为了求解该问题,开发了一种GRASP启发式算法;最后,通过一个实例分析说明了所提出方法的可行性和实际应用价值。     

Flexible simulation of competing risks data following prespecified subdistribution hazards

In recent years different approaches for the analysis of time-to-event data in the presence of competing risks, i.e. when subjects can fail from one of two or more mutually exclusive types of event, were introduced. Different approaches for the analysis of competing risks data, focusing either on cause-specific or subdistribution hazard rates, were presented in statistical literature. Many new approaches use complicated weighting techniques or resampling methods, not allowing an analytical evaluation of these methods. Simulation studies often replace analytical comparisons, since they can be performed more easily and allow investigation of non-standard scenarios. For adequate simulation studies the generation of appropriate random numbers is essential. We present an approach to generate competing risks data following flexible prespecified subdistribution hazards. Event times and types are simulated using possibly time-dependent cause-specific hazards, chosen in a way that the generated data will follow the desired subdistribution hazards or hazard ratios, respectively.     

Truncation invariant copulas and a testing procedure

ABSTRACT

The class of bivariate copulas that are invariant under truncation with respect to one variable is considered. A simulation algorithm for the members of the class and a novel construction method are presented. Moreover, inspired by a stochastic interpretation of the members of such a class, a procedure is suggested to check whether the dependence structure of a given data set is truncation invariant. The overall performance of the procedure has been illustrated on both simulated and real data.     

A new metamodel-based method for solving semi-expensive simulation optimization problems

In this article, a new algorithm for rather expensive simulation problems is presented, which consists of two phases. In the first phase, as a model-based algorithm, the simulation output is used directly in the optimization stage. In the second phase, the simulation model is replaced by a valid metamodel. In addition, a new optimization algorithm is presented. To evaluate the performance of the proposed algorithm, it is applied to the (s,S) inventory problem as well as to five test functions. Numerical results show that the proposed algorithm leads to better solutions with less computational time than the corresponding metamodel-based algorithm.     

Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey

Small area statistics obtained from sample survey data provide a critical source of information used to study health, economic, and sociological trends. However, most large-scale sample surveys are not designed for the purpose of producing small area statistics. Moreover, data disseminators are prevented from releasing public-use microdata for small geographic areas for disclosure reasons; thus, limiting the utility of the data they collect. This research evaluates a synthetic data method, intended for data disseminators, for releasing public-use microdata for small geographic areas based on complex sample survey data. The method replaces all observed survey values with synthetic (or imputed) values generated from a hierarchical Bayesian model that explicitly accounts for complex sample design features, including stratification, clustering, and sampling weights. The method is applied to restricted microdata from the National Health Interview Survey and synthetic data are generated for both sampled and non-sampled small areas. The analytic validity of the resulting small area inferences is assessed by direct comparison with the actual data, a simulation study, and a cross-validation study.