恐怖主义犯罪预防对策研究

鉴于恐怖主义犯罪的严重危害性及鲜明的政治色彩，如何切实有效地防范与惩治恐怖主义犯罪已成为我国当前社会关注的焦点问题。对恐怖主义犯罪要综合运用政治、法律、宗教、民族等各种社会政策与法律政策，健全和完善反恐预防体系，标本兼治、打防结合，才是彻底根除或者至少是有效遏制或减少恐怖主义犯罪的有效手段。     

我国研究型教师队伍建设的对策研究

建设研究型教师队伍在实施“科教兴国”战略方面具有基础性的重要作用。我国应当采取切实的措施，建设好数量充足的高素质科研师资队伍，为实施素质教育，培育创造型人才奠定师资基础。     

确立精品意识,创立品牌期刊

高校学报要创立品牌期刊，一要坚持正确的舆论导向；二要确立精品意识，争创品牌期刊；三要坚持学报的特色化；四要在学报编排上坚持国际化与规范化；五要全面实现编辑技术手段现代化。     

经济增长、模型再造与农村经济增长方式的转变

经济增长理论是社会经济发展的一种理论反映,源于斯密和李嘉图为代表的古典经济学派,是二战后在发达国家广泛流行的经济理论。但是,发达国家经济学家自身存在的认识论缺陷使其不可能建立科学完整的经济增长理论及模型,因此还必须遵循马克思的方法论从理论为实际的反映入手建立科学模型,并以此指导中国社会经济增长实践,特别是农村经济增长方式的转变实践。     

治理通货膨胀的宏观调控分析模型

本文以我国通货膨胀的形成机制为依据,分析通货膨胀与宏观调控手段之间的关系,用此关系建成计量经济模型和辨识出模型的参数,并做了解释和检验.     

教师教学效能的价值论审思

教师教学效能并非纯粹心理学意义上的效能感,而是主体感受与具体情景的交融。对教学效能的价值论审思应超越单一的心理学认知或信念范畴,回归教育学本体的价值诉求,提升教师主体存在的内生动力、学生主体发展的外发动力和"人学"为体的永恒动力,进而通过重构教师自我认知体系、主动建构教师专业发展体系、不断完善教学场域文化体系等价值手段促进其价值实现。     

The empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior under Stein's loss function

For the hierarchical Poisson and gamma model, we calculate the Bayes posterior estimator of the parameter of the Poisson distribution under Stein's loss function which penalizes gross overestimation and gross underestimation equally and the corresponding Posterior Expected Stein's Loss (PESL). We also obtain the Bayes posterior estimator of the parameter under the squared error loss and the corresponding PESL. Moreover, we obtain the empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior by two methods. In numerical simulations, we have illustrated: The two inequalities of the Bayes posterior estimators and the PESLs; the moment estimators and the Maximum Likelihood Estimators (MLEs) are consistent estimators of the hyperparameters; the goodness-of-fit of the model to the simulated data. The numerical results indicate that the MLEs are better than the moment estimators when estimating the hyperparameters. Finally, we exploit the attendance data on 314 high school juniors from two urban high schools to illustrate our theoretical studies.     

Nonparametric Bayesian inference for multivariate density functions using Feller priors

Multivariate density estimation plays an important role in investigating the mechanism of high-dimensional data. This article describes a nonparametric Bayesian approach to the estimation of multivariate densities. A general procedure is proposed for constructing Feller priors for multivariate densities and their theoretical properties as nonparametric priors are established. A blocked Gibbs sampling algorithm is devised to sample from the posterior of the multivariate density. A simulation study is conducted to evaluate the performance of the procedure.     

A Method to Handle Zero Counts in the Multinomial Model

In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts.     

Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation

Bridge penalized regression has many desirable statistical properties such as unbiasedness, sparseness as well as ‘oracle’. In Bayesian framework, bridge regularized penalty can be implemented based on generalized Gaussian distribution (GGD) prior. In this paper, we incorporate Bayesian bridge-randomized penalty and its adaptive version into the quantile regression (QR) models with autoregressive perturbations to conduct Bayesian penalization estimation. Employing the working likelihood of the asymmetric Laplace distribution (ALD) perturbations, the Bayesian joint hierarchical models are established. Based on the mixture representations of the ALD and generalized Gaussian distribution (GGD) priors of coefficients, the hybrid algorithms based on Gibbs sampler and Metropolis-Hasting sampler are provided to conduct fully Bayesian posterior estimation. Finally, the proposed Bayesian procedures are illustrated by some simulation examples and applied to a real data application of the electricity consumption.