Subsampling Variance Estimation for Non-stationary Spatial Lattice Data

ABSTRACT.  Most proposed subsampling and resampling methods in the literature assume stationary data. In many empirical applications, however, the hypothesis of stationarity can easily be rejected. In this paper, we demonstrate that moment and variance estimators based on the subsampling methodology can also be employed for different types of non-stationarity data. Consistency of estimators are demonstrated under mild moment and mixing conditions. Rates of convergence are provided, giving guidance for the appropriate choice of subshape size. Results from a small simulation study on finite-sample properties are also reported.     

Bootstrapping data with multiple levels of variation

The authors consider general estimators for the mean and variance parameters in the random effect model and in the transformation model for data with multiple levels of variation. They show that these estimators have different distributions under the two models unless all the variables have Gaussian distributions. They investigate the asymptotic properties of bootstrap procedures designed for the two models. They also report simulation results and illustrate the bootstraps using data on red spruce trees.     

A mixture model for random graphs

The Erdös–Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.     

Using machine learning to identify early predictors of adolescent emotion regulation development

As 20% of adolescents develop emotion regulation difficulties, it is important to identify important early predictors thereof. Using the machine learning algorithm SEM-forests, we ranked the importance of (87) candidate variables assessed at age 13 in predicting quadratic latent trajectory models of emotion regulation development from age 14 to 18. Participants were 497 Dutch families. Results indicated that the most important predictors were individual differences (e.g., in personality), aspects of relationship quality and conflict behaviors with parents and peers, and internalizing and externalizing problems. Relatively less important were demographics, bullying, delinquency, substance use, and specific parenting practices—although negative parenting practices ranked higher than positive ones. We discuss implications for theory and interventions, and present an open source risk assessment tool, ERRATA.     

Setting the control limit at release for stability assurance

A common task in quality control is to determine a control limit for a product at the time of release that incorporates its risk of degradation over time. Such a limit for a given quality measurement will be based on empirical stability data, the intended shelf life of the product and the stability specification. The task is particularly important when the registered specifications for release and stability are equal. We discuss two relevant formulations and their implementations in both a frequentist and Bayesian framework. The first ensures that the risk of a batch failing the specification is comparable at release and at the end of shelf life. The second is to screen out batches at release time that are at high risk of failing the stability specification at the end of their shelf life. Although the second formulation seems more natural from a quality assurance perspective, it usually renders a control limit that is too stringent. In this paper we provide theoretical insight in this phenomenon, and introduce a heat-map visualisation that may help practitioners to assess the feasibility of implementing a limit under the second formulation. We also suggest a solution when infeasible. In addition, the current industrial benchmark is reviewed and contrasted to the two formulations. Computational algorithms for both formulations are laid out in detail, and illustrated on a dataset.     

Impact of the tariff concessions of the RCEP agreement on the structure and evolution mechanism of manufacturing trade networks

In the world’s largest free trade area, the Regional Comprehensive Economic Partnership (RCEP) agreement will gradually eliminate tariffs on over 90% of member countries’ goods over the next 36 years. We construct a tariff policy effects evaluation framework based on the complex network theory. In this framework, the standard Global Trade Analysis Project (GTAP) model is recursively extended to generate forecasted data of bilateral trade. Based on this, we model the RCEP manufacturing trade networks and analyze the response of its core–periphery structure to the tariff concessions. Then, we evaluate policy effects on the evolution of trade patterns based on motif analysis. Finally, we construct separable temporal exponential family random graph models (STERGM) to explore the influence path and degree of the new tariff agreement on the evolutionary mechanisms of trade networks.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

A Review of the packing problem

The random pazking problem has been of interest to inoestigsccrs in seveal disciplines , Physical chemists have investigaced such models in two and three dimensions, Because of aralytical difficulties, one-dimensional analogacs have been explored and theseare referred to as the parking problem, A number of results areexplored and attempts are made to tie them together, Applicationsare also highlighted.     

A new look at clustering coefficients with generalization to weighted and multi-faction networks

In this paper we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks the values obtained for commonly considered social networks are in sharp contrast to those obtained, for instance, by the method of Opsahl (2013), and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered.     

多重随机环境下跨国采购和定价联合决策模型

在批发价、汇率和需求的三重随机波动环境下,考虑跨国采购与定价联合决策问题。本文主要利用随机过程来刻画批发价和汇率的随机性,并将两者的统计规律性纳入带有随机需求的决策模型的理论框架,进而给出跨国采购和定价联合决策模型及其可靠性评估机制。模型的相关结论表明:其一、不同周期的最优策略受到批发价与汇率波动的交互影响,当批发价和汇率波动性的重叠效应具有下降趋势时,销售商所采取的最优策略为增加一定的采购量,以实现系统的期望利润目标;其二、当批发价与汇率统计结构的属性为不可约遍历马氏链时,随机系统的下行风险具有良好的稳定性;其三、决策者可以通过本文提出靶向成本区间的上穿评估机制刻画随机系统的成本涨幅特征及其变化趋势,以解决供应商的最佳选择问题,从而达到了规避风险的运营目标。