对人生价值观几个问题的探讨

探讨人生价值观的热门话题,应明确和解决３个问题：１．价值、人的价值及人生价值的概念;２．马克思主义关于人生价值观的两个基本命题,即个人价值和社会价值的统一、道义价值和功利价值的统一;３．“人生的价值在于贡献”的价值导向     

复调和函数的混合边值问题

利用Ｆｏｕｒｉｅｒ方法得到复调和函数的混合边值问题解的表示式及解的存在性定理并讨论了解的唯一性．     

论时务学堂的历史地位、恢复价值与开发措施

在两型社会和国际文化名城建设中,长沙须深入挖掘独特的历史文化资源,大力发展文化产业。时务学堂具有独特的历史地位和文化价值,值得恢复重建。要将保护与开发相结合,通过现代化经营,创造理想的社会效益与经济效益。     

习近平总书记关于高校体育工作重要论述的逻辑意蕴与实践路径

习近平总书记关于高校体育工作重要论述是新时代中华民族所认同体育文化的价值取向、精神气质、思维方式与行动纲要的总和,它是大学生思想教育工作发展创新的内在需要。高校通过以中华体育文化精神为基石,融入社会主义主流价值;优化体育文化育人环境,让体育文化叫好又叫座;深入挖掘体育文化活动内涵,打造经典体育文化品牌;丰富体育文化载体形式,使体育文化育人产生倍增效果,多种实践途径践行习近平总书记关于高校体育工作的重要论述,促推中华民族体育强国梦的实现。     

大学生学科文化自信培育的价值逻辑及路径探析 ——以河北工程大学地球科学与工程学院为例

学科文化是学科的灵魂,是学科不断发展创新的精神源泉和内在动力。学科文化自信是文化自信的重要组成部分,培育大学生文化自信应着眼全局,整体推进。学科文化自信与文化自信具有统一性,与核心价值观培育具有同构性,与担当民族复兴大任具有生成性。培育大学生学科文化自信,要深入挖掘和整合学科文化内涵,不断创新和发展培育手段,最终达到浸润和形塑博学自信的新时代大学生的目标。学科文化自信的培育可以达到激发学生学术热情,促进学生和学科全面发展,弘扬社会主义核心价值观,坚定理想信念以及践行使命担当的效用。     

延安时期党内政治生态建设理论创新及其时代价值

延安时期,中国共产党的领袖们在党内政治生态建设方面进行了大量卓有成效的理论创新,阐明了党内政治生态建设的内在动力、基本规范、重要途径和根本保障。当前,我们党在全面从严治党过程中,严肃党内政治生活,净化党内政治生态,仍需要从延安时期党内政治生态建设历史经验中汲取营养。     

基于Markov区制转换模型的极值风险度量研究

将马尔科夫区制转换模型与极值理论相结合研究金融风险度量问题.首先用SWARCH-t模型捕捉收益率序列的剧烈波动和结构变换特征,然后将收益序列转化为标准残差序列,在此基础上通过SWARCH-t模型与极值理论相结合拟合标准残差的尾部分布,进而构建基于SWARCH- t- EVT的动态VaR模型,最后对模型的有效性进行检验.研究表明,SWARCH-t-EVT模型能够有效识别上证综指的波动区制特征,且能有效合理地测度上证综指收益风险,尤其在高的置信水平下表现更好.     

Evaluating lifetime performance for the Pareto model with censored and imprecise information

A lifetime capability index L _tp has been proposed to measure the business lifetime performance, wherein output lifetime measurements are assumed to be precise from the Pareto model with censored information. In the present study, we study a more realistic situation where the lifetime output data are imprecise. The approach developed by Buckley [Fuzzy system, Soft Comput. 9 (2005), pp. 757–760; Fuzzy statistics: Regression and prediction, Soft Comput. 9 (2005), pp. 769–775] incorporated with some extensions (a set of confidence intervals, one on top of the other), is used to construct the triangular-shaped fuzzy number for the fuzzy estimate of the L _tp. With the sampling distribution of the unbiased estimator of the L _tp, two useful fuzzy inference criteria, its critical value and fuzzy p-value are obtained to assess the lifetime performance. The presented methodology can handle the lifetime performance assessment on the condition that sample lifetime data are involved with imprecise information, classifying the lifetime performance with the three-decision rule. With different preset requirements and a certain degree of imprecise data, we also develop a four quadrants decision-making plot where managers can easily simultaneously visualize several important features of lifetime performance for making a decision. An example of business lifetime data is given to illustrate the applicability of the proposed method.     

A- and D-optimal progressive Type-II censoring designs based on Fisher information

Fisher information about multiple parameters in a progressively Type-II censored sample is discussed. A representation of the Fisher information matrix in terms of the hazard rate of the baseline distribution is established which can be used for efficient computation of the Fisher information. This expression generalizes a result of Zheng and Park [On the Fisher information in multiply censored and progressively censored data, Comm. Statist. Theory Methods 33 (2004), pp. 1821–1835] for Fisher information about a single parameter. The result is applied to identify A- and D-optimal censoring plans in a progressively Type-II censored experiment. For illustration, extreme value, normal, and Lomax distributions are considered.     

Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria

There is currently much discussion about lasso-type regularized regression which is a useful tool for simultaneous estimation and variable selection. Although the lasso-type regularization has several advantages in regression modelling, owing to its sparsity, it suffers from outliers because of using penalized least-squares methods. To overcome this issue, we propose a robust lasso-type estimation procedure that uses the robust criteria as the loss function, imposing L₁-type penalty called the elastic net. We also introduce to use the efficient bootstrap information criteria for choosing optimal regularization parameters and a constant in outlier detection. Simulation studies and real data analysis are given to examine the efficiency of the proposed robust sparse regression modelling. We observe that our modelling strategy performs well in the presence of outliers.