《国语》的编撰意图及其文学价值

在中国文学史上,《国语》一向不被人重视。其实不论是从史学角度,还是从文学角度,《国语》都是很值得深入研究的。本文通过对《国语》编撰意图的分析,认为其文学史料不是简单的拼凑,而是紧紧围绕礼治、民本、忠恕、正名等思想选择的。此外,《国语》一书结构的安排,也不只是遵循周王室与各诸侯国关系远近、先华夏后蛮夷的原则,而是另有深意,即反映礼乐崩坏的前因后果。《国语》一书因此成为有机的整体,文学和史学价值也进一步提高。     

考虑脉冲现象的复杂供应链系统及其稳定策略

供应链系统是一个复杂的系统,一方面由于时间延迟、信息不对称及反馈机制等,会产生牛鞭效应,另一方面供应链具有需求突增或骤减的脉冲特点,上述现象都可能导致供应链系统混沌,而现有的供应链模型仅考虑了“牛鞭效应”,并未考虑脉冲现象对供应链系统的影响。文章建立了考虑脉冲现象的供应链模型,分析了脉冲现象对供应链系统的影响,并给出了使供应链系统稳定的策略,讨论了参数扰动对该模型的影响,通过数值算例验证了稳定策略的有效性。研究发现:脉冲现象对供应链系统有着显著影响,但只要选取恰当的脉冲强度和脉冲时间间隔,供应链系统是可以稳定的。     

老字号品牌价值共创机理及对策研究——基于服务主导逻辑视角

将服务主导逻辑的思维范式引入老字号企业中来,结合消费文化和社会情景价值理论探讨该领域中品牌价值的共创机理,基于理论假设构建研究框架。以北京、上海、福建三个地区的不同行业老字号品牌企业为实证研究对象,对企业员工和消费者进行访谈和问卷调查。通过修正的初始量表,基于PLS-SEM方法对收集的309份有效问卷调查数据应用SPSS22.0和Smart PLS2.0进行分析,验证消费者情感价值共创的中介作用及互联网媒介交互的调节作用。分析结果表明,消费者与老字号企业共同创造价值,历史悠久的老字号品牌企业关注基于互联网媒介下不同利益相关者的互动和协同才能创造品牌价值共创的最理想点,最后为老字号品牌管理提供有益的建议和应对策略。     

为历史增添精神重量与文化资源——王充闾散文创作论纲

将王充闾的散文创作置于文学历史的发展过程之中,从思想含量、文化价值、文学意义、主体特征四个方面,展开梳理、归纳和阐释,就中凸现其历史与美学的特殊贡献。     

中国环境公益诉讼之兴起与走势——基于环境正义与环境诉讼价值进路的分析

在我国,环境污染及其造成的损害日益严重,客观上对损害救济的相关法律提出了变革与发展的要求。然而,传统环境侵权诉讼在原告资格、诉讼事由和诉讼主体——环保团体的诉讼资格方面面临诸多困境。环境公益诉讼在原告的起诉资格、诉讼标的、诉讼事由以及诉讼性质等方面,弥补了传统环境侵权诉讼的缺陷,在实现环境正义、保护环境公益、救济环境损害方面起到了不可替代的作用。     

Multivariate mixtures of normals with unknown number of components

We present full Bayesian analysis of finite mixtures of multivariate normals with unknown number of components. We adopt reversible jump Markov chain Monte Carlo and we construct, in a manner similar to that of Richardson and Green (1997), split and merge moves that produce good mixing of the Markov chains. The split moves are constructed on the space of eigenvectors and eigenvalues of the current covariance matrix so that the proposed covariance matrices are positive definite. Our proposed methodology has applications in classification and discrimination as well as heterogeneity modelling. We test our algorithm with real and simulated data.     

BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE

This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature.     

A hierarchical model for extreme wind speeds

Summary.  A typical extreme value analysis is often carried out on the basis of simplistic inferential procedures, though the data being analysed may be structurally complex. Here we develop a hierarchical model for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location. A Gaussian model for the random effects exploits the meteorological structure in the data, enabling increased precision for inferences at individual sites and in individual seasons. The Bayesian framework that is adopted is also exploited to obtain predictive return level estimates at each site, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes that are involved.     

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

Abstract.  The Andersson–Madigan–Perlman (AMP) Markov property is a recently proposed alternative Markov property (AMP) for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced Lauritzen–Wermuth–Frydenberg Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.     

Bayesian palaeoclimate reconstruction

Summary.  We consider the problem of reconstructing prehistoric climates by using fossil data that have been extracted from lake sediment cores. Such reconstructions promise to provide one of the few ways to validate modern models of climate change. A hierarchical Bayesian modelling approach is presented and its use, inversely, is demonstrated in a relatively small but statistically challenging exercise: the reconstruction of prehistoric climate at Glendalough in Ireland from fossil pollen. This computationally intensive method extends current approaches by explicitly modelling uncertainty and reconstructing entire climate histories. The statistical issues that are raised relate to the use of compositional data (pollen) with covariates (climate) which are available at many modern sites but are missing for the fossil data. The compositional data arise as mixtures and the missing covariates have a temporal structure. Novel aspects of the analysis include a spatial process model for compositional data, local modelling of lattice data, the use, as a prior, of a random walk with long-tailed increments, a two-stage implementation of the Markov chain Monte Carlo approach and a fast approximate procedure for cross-validation in inverse problems. We present some details, contrasting its reconstructions with those which have been generated by a method in use in the palaeoclimatology literature. We suggest that the method provides a basis for resolving important challenging issues in palaeoclimate research. We draw attention to several challenging statistical issues that need to be overcome.