从《大学衍义补》看邱濬对“华夷之辨”的释读

邱濬的《大学衍义补》一书以其恢弘的史学视角和满富激情的历史评论,对儒家原典中的＂华夷之辨＂问题进行了详细的整理和补释,该书不仅重点诠释了中国传统对外思想发展过程的一些主要特征,而且对古代外交政策中的得失给予了辩证而理性的评价。尽管邱濬受时代影响,对前代历史尚存一些偏见,但是,作为一代政治家和思想巨匠,邱濬的历史批判精神、勇于提出问题的魄力和强烈的忧患意识仍值得我们充分肯定。     

中日水污染刑事立法比较研究

相对日本完善的水污染刑事立法,我国对水污染犯罪仅作了原则性规定,不仅缺乏独立的罪名,而且具有处罚范围狭窄,刑事处罚不力等弊端,从而影响到对水污染犯罪的刑事制裁。借鉴日本经验,我国应在以下几个方面对水污染刑事立法加以完善：设置独立的水污染罪;规定故意和过失两种犯罪形态;明确水污染刑事制裁范围,并设置水污染罪危险犯;加大水污染犯罪的处罚力度并充分发挥罚金刑的作用。     

基于波函数的证券市场回报率分析

利用有限深方势阱中自由粒子的波函数,结合上证综指数据,得到了与实际相符合的证券市场回报率的分布函数。该函数能与实际数据很好地符合,验证了波函数模型的有效性和稳健性,表明证券市场回报率具有一定的量子效应。分布函数印证了市场回报率分布具有尖峰、不对称特征。与伦敦市场的比较发现,上海证券市场具有较高的回报率,但也存在较大的风险。这说明当前中国证券市场的投机性和风险性共存,同时也说明中国经济发展状况良好,但证券市场稳定性仍需继续加强。     

森林公园旅游业的发展对就业的影响分析

为了定量分析森林公园旅游业的发展对就业的拉动作用,首先分析了森林公园旅游业对就业的影响机理,并基于历史数据和回归模型,预测了未来几年森林公园旅游业的发展对就业的影响力。研究表明,中国森林公园的发展潜力巨大,森林公园旅游业的直接就业弹性在0．3～0．5之间,对就业的拉动效果显著。     

基于ALTRANS模型的我国生物燃料发展潜力研究

生物燃料作为可再生燃料,其开发和利用将对解决我国能源和环境问题产生积极的作用,但其发展面临着资源和技术两方面的挑战,需将其纳入交通能源和整体能源体系中进行综合性研究和评价,这对科学确立生物燃料发展战略和技术路线具有重要的战略意义。为此,构建了基于MAED和MESSAGE模型的Tsinghua-ALTRANS模型,通过对生物燃料的3类潜力、3层结构情景进行定量分析,识别出发展生物燃料的贡献和所面临的挑战。     

Analysis of ordinal outcomes with longitudinal covariates subject to missingness

We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable. A generalized EM algorithm is used to obtain maximum likelihood estimates.     

随机加权交叉效率下的资源分配问题研究

资源在经济发展中的地位变得越来越重要,本文针对资源分配中存在的某些影响因素是随机的,提出利用随机DEA方法,从三方面综合分析研究资源分配问题,即从生产效率、投入产出弹性和生产潜能。提出并建立了新的随机加权交叉效率计算模型,建立了潜在生产能力模型,给出了规模回报计算公式。综合三个影响因素,建立了新的资源分配权重计算模型。经算例验证,其计算结果令人满意。     

A generalized bivariate Bernoulli model with covariate dependence

Dependence in outcome variables may pose formidable difficulty in analyzing data in longitudinal studies. In the past, most of the studies made attempts to address this problem using the marginal models. However, using the marginal models alone, it is difficult to specify the measures of dependence in outcomes due to association between outcomes as well as between outcomes and explanatory variables. In this paper, a generalized approach is demonstrated using both the conditional and marginal models. This model uses link functions to test for dependence in outcome variables. The estimation and test procedures are illustrated with an application to the mobility index data from the Health and Retirement Survey and also simulations are performed for correlated binary data generated from the bivariate Bernoulli distributions. The results indicate the usefulness of the proposed method.     

On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter

In this article we describe methods for obtaining the predictive distributions of outcome gains in the framework of a standard latent variable selection model. Although most previous work has focused on estimation of mean treatment parameters as the method for characterizing outcome gains from program participation, we show how the entire distributions associated with these gains can be obtained in certain situations. Although the out-of-sample outcome gain distributions depend on an unidentified parameter, we use the results of Koop and Poirier to show that learning can take place about this parameter through information contained in the identified parameters via a positive definiteness restriction on the covariance matrix. In cases where this type of learning is not highly informative, the spread of the predictive distributions depends more critically on the prior. We show both theoretically and in extensive generated data experiments how learning occurs, and delineate the sensitivity of our results to the prior specifications. We relate our analysis to three treatment parameters widely used in the evaluation literature—the average treatment effect, the effect of treatment on the treated, and the local average treatment effect—and show how one might approach estimation of the predictive distributions associated with these outcome gains rather than simply the estimation of mean effects. We apply these techniques to predict the effect of literacy on the weekly wages of a sample of New Jersey child laborers in 1903.