电视栏目的独立视角与平民化诉求

由民生新闻带动的社会民生类节目,以其百姓的立场与视角、平民化的叙述方式,在日趋白热化的电视节目竞争中占得一席之地。然而,平民化中的＂低＂视角、＂浅＂内涵、＂俗＂文化,也把社会民生类节目引向低俗化,这一部分归因于节目本身在选题内容、表达方式、监督职能与互动设置上的过失性＂越位＂与蓄意性＂反越位＂,更深层次地归因于媒体追逐经济利益与满足受众低俗趣味的双重机制,致使社会民生类节目游走在平民化与低俗化之间。     

血墨丹青--恽寿平生平艺术初探

清代初期画坛六大家之一的恽寿平,早年抗清,失败后誓不入仕,隐居武进老宅,倾情诗书画,既为谋生,亦是萌志。中年赁居常州城中白云溪畔,先借画室,后扩为家,并终老于此。文章阐述了他与另一书画大师王翚艺术互慕、事业相助的终生友谊,总结了其绘画艺术特色,即独创“没骨花卉”技法,并引领“常州画派”风靡清代早中期神州画坛。     

关于韩国电影的几点思考

文章通过对韩国电影深入切近的剖析,从传统文化和本土经验的角度,对商业机制下的电影文本作回归影音本质的诠释。在比较历史传统的背景下以人性、爱情、细节为触点探求民族精神气质所蕴涵的丰厚艺术包容量。     

文化遗址保护与关联产业和谐共生机制实证研究

以大唐西市为例,研究了文化遗址保护与关联产业和谐共生关系。研究认为,文化遗址保护与关联产业和谐共生是互辅共进的持续发展模式,其包括三大机制:价值生成机制是和谐共生的动力源泉,运行机制是和谐共生的坚实基础,利益分配机制是和谐共生的基本保障。研究结果表明:文化遗址保护与关联产业和谐共生模式值得大力推广,关键要形成以构建文化遗址保护与关联产业和谐共生的价值生成、协调运行和利益分配三大核心机制。     

随机效应空间滞后单指数面板模型

对随机效应空间滞后单指数面板模型,本文构建了该模型的截面极大似然估计方法,从理论证明和数值模拟两方面分别考察了其估计量的大样本性质和小样本表现。研究结果表明:(1)在大样本条件下,估计量均具有一致性,并且参数估计量具有渐近正态性。(2)在小样本条件下,各估计量依然具有良好的表现,其精度随着样本容量的增加而提高;空间权重矩阵结构的复杂性对空间相关系数的估计量影响较大,但对其他估计量的影响较小。     

商业银行集团客户统一授信额度的优化配置研究

企业集团是商业银行重要的贷款客户,商业银行面临企业集团的授信业务风险尤为突出。在结构化模型的框架下,考虑统一授信额度的约束,基于对违约风险控制和贷款收益管理的多目标决策,构建了企业集团成员企业授信额度优化配置模型。示例分析表明,在考虑不同目标重要性的前提下,使用如遗传算法等最优化求解方法,可得到对成员企业授信额度的优化配置方案,从而有助于商业银行积极主动的防范集团客户的信贷风险。     

信息披露最优化与交易效率——一个弱势个体与强势个体交换的例证

本文以中小企业融资市场为例,应用数理模型揭示了弱势个体通过集体形式披露信息从而提高交易效率的微观机理。研究发现,弱势个体在同强势个体进行交易时,不仅存在信息披露最优化问题,还存在着规模效应不足的问题,这致使两者在现实中难以实现交易。研究表明:1.一定程度上信息不对称能使弱势个体与强势个体在交易中实现双赢;2. 弱势个体在无法正常披露信息时,通过捆绑以强势集体形式披露信息,不仅可以改善强弱双方的信息不对称状况,而且可以把弱势个体隐匿型信息内部化,以利于同强势个体交易,从而提高交易效率;3. 集体披露信息机制还能够创造规模效应。     

Semiparametric Regression Analysis of Longitudinal Skewed Data

In this paper, we develop a semiparametric regression model for longitudinal skewed data. In the new model, we allow the transformation function and the baseline function to be unknown. The proposed model can provide a much broader class of models than the existing additive and multiplicative models. Our estimators for regression parameters, transformation function and baseline function are asymptotically normal. Particularly, the estimator for the transformation function converges to its true value at the rate n ^{? 1 ∕ 2}, the convergence rate that one could expect for a parametric model. In simulation studies, we demonstrate that the proposed semiparametric method is robust with little loss of efficiency. Finally, we apply the new method to a study on longitudinal health care costs.     

Asymptotics for Tail Probability of Random Sums with a Heavy-Tailed Number and Dependent Increments

This article obtains the asymptotics for the tail probability of random sums, where the random number and the increments are all heavy tailed, and the increments follow a certain wide dependence structure. This dependence structure can contain some commonly used negatively dependent random variables as well as some positively dependent random variables.     

Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics

We study sequential Bayesian inference in stochastic kinetic models with latent factors. Assuming continuous observation of all the reactions, our focus is on joint inference of the unknown reaction rates and the dynamic latent states, modeled as a hidden Markov factor. Using insights from nonlinear filtering of continuous-time jump Markov processes we develop a novel sequential Monte Carlo algorithm for this purpose. Our approach applies the ideas of particle learning to minimize particle degeneracy and exploit the analytical jump Markov structure. A motivating application of our methods is modeling of seasonal infectious disease outbreaks represented through a compartmental epidemic model. We demonstrate inference in such models with several numerical illustrations and also discuss predictive analysis of epidemic countermeasures using sequential Bayes estimates.