鲁迅与波德莱尔的话语共鸣

《野草》和《巴黎的忧郁》是鲁迅和波德莱尔的散文诗集,两位伟大的作家选择了相同的文体,运用象征手法,选择特有的意象,表达时代的抑郁和苦闷。由此形成了两者的话语共鸣,体味出新的审美典范。散文诗以其特殊的话语形态表达作者的心理情感及其想像空间,鲁迅与波德莱尔一方面具备写作散文诗的特质,另一方面时代也促成了他们选择这样的话语形态,由此透过两位大作家的文字,我们体味到两者文本之中的话语共鸣——精神的忧郁、心灵的苦闷;同时也体味到世界文学的共鸣。     

教学实习与暑期社会实践对接的可行性研究

通过分析教学实习和暑期社会实践不能适应人才培养需求的原因,阐述了教学实习与暑期社会实践活动对接的可行性,归纳出两者有机结合必须符合教育教学工作和学生专业学习实际,实现学校人才培养目标与社会、企业用人需求相统一等原则,指出把暑期社会实践和教学实习有机结合在一起,实现优势互补是提高实践教育效果的重要方式。     

网络集群的自组织特征 —— 以“南京梧桐树事件”的微博维权为个案

近年来,伴随着急剧的社会结构转型,我国新的社会身份和社会认同群体也随之产生,在网络中的政治表达和利益维护活动增多,在这些网络讨论的集群内部,参与者会自发形成一定的秩序,推动事件的进行。本研究利用自组织理论作为分析框架,选取"南京梧桐树事件"的微博维权集群为研究对象,重点考察网络集群参与者之间从无序到有序的互动过程、整个系统的演变规律及其与外界的互动机制,由此总结出网络集群中的自组织特征,从而相对准确地把握虚拟社区系统的发展、演化阶段,为更好地理解互联网的社会功能起到借鉴作用,也为政府有关部门做好群体事件的预防工作提供理论和策略上的支持。     

Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.     

A misspecification test for the higher order co-moments of the factor model

The traditional estimation of higher order co-moments of non-normal random variables by the sample analog of the expectation faces a curse of dimensionality, as the number of parameters increases steeply when the dimension increases. Imposing a factor structure on the process solves this problem; however, it leads to the challenging task of selecting an appropriate factor model. This paper contributes by proposing a test that exploits the following feature: when the factor model is correctly specified, the higher order co-moments of the unexplained return variation are sparse. It recommends a general to specific approach for selecting the factor model by choosing the most parsimonious specification for which the sparsity assumption is satisfied. This approach uses a Wald or Gumbel test statistic for testing the joint statistical significance of the co-moments that are zero when the factor model is correctly specified. The asymptotic distribution of the test is derived. An extensive simulation study confirms the good finite sample properties of the approach. This paper illustrates the practical usefulness of factor selection on daily returns of random subsets of S&P 100 constituents.     

Lower bound of average centered L2-discrepancy for U-type designs

Uniform designs are widely used in various scientific investigations and industrial applications. By considering all possible level permutation of the factors, a connection between average centered L₂-discrepancy and generalized wordlength pattern for asymmetrical fractional factorial designs is derived. Moreover, we present new lower bounds to the average centered L₂-discrepancy for symmetrical and asymmetrical U-type designs. For illustration of the theoretical results, the lower bounds for symmetrical and asymmetrical U-type designs are tabulated, and numerical results indicate that our lower bounds behave well and can be recommended for use in practice.     

The laws of large numbers for Pareto-type random variables with infinite means

In this paper, we consider the laws of large numbers for NSD random variables satisfying Pareto-type distributions with infinite means. Based on the Pareto-Zipf distributions, some weak laws of large numbers for weighted sums of NSD random variables are obtained. Meanwhile, we show that a weak law for Pareto-Zipf distributions cannot be extended to a strong law. Furthermore, based on the two tailed Pareto distribution, a strong law of large numbers for weighed NSD random variables is presented. Our results extend the corresponding earlier ones.     

Semiparametric Bayesian analysis for longitudinal mixed effects models with non-normal AR(1) errors

Statistics and Computing - This paper studies Bayesian inference on longitudinal mixed effects models with non-normal AR(1) errors. We model the nonparametric zero-mean noise in the autoregression...     

环境信息不对称、机构投资者实地调研与企业环境治理

基于信息不对称理论,本文以2013-2016年深交所重污染行业上市公司为研究样本,分析了机构投资者实地调研对企业环境治理的影响及其作用机制。研究发现,机构投资者实地调研对企业环境治理具有积极的推动作用;在环境信息披露较差以及本地子公司较多的企业中,机构投资者实地调研的环境治理作用更为明显。进一步考察实地调研对企业环境治理的影响路径发现,机构投资者实地调研推动了企业环境治理的资本市场监督和媒体监督。本文的研究验证了信息不对称理论在环境治理中的作用,拓宽了实地调研经济后果的研究领域,对于深入理解并推动我国企业环境治理具有重要的现实意义。     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.