北宋书序作者所选择的传播因素分析

书序具有重要的传播作用,以北宋诗文集序为对象考察,书序作者在选择推动传播的因素时,对作品艺术性、知识性等文本因素的关注不如对作家身份、生平轶事等非文本因素普遍。其中原因,从书序作者的操作层面来看,是因为在书序中进行文艺批评是很难把握的;从面对读者的传播效果层面来分析,是因为非文本因素较之文本相关的评论因素可以产生显著的传播效果。于是,非文本因素在文学传播中变成了一位无形的操纵者,虽然它不是长远的传播之道,但却常常起着奇妙的传播作用,从而给书序这一文体增加活力。     

数值解法中的单元形状函数技巧

本文介绍从一组互不相关的函数中推导数值法中单元形状函数的简便而有效的算法,以特定的一维内插方法导出各种二维、三维的内插方法,它们对奇异性的模拟是有益的;为了适用于延伸到无限远的单元,假设形状函数类似于Lagrange多项式,但包含了一个按指数衰减的项。     

肾叶唐松草微量元素和氨基酸的测定

本文利用原子吸收光谱法测定了中药肾叶唐松草中所含14种微量元素,利用氨基酸分析仪对它所含的氨基酸进行了鉴定及含量测定,并对根、茎、叶等不同部位的微量元素和氨基酸含量分别做了比较。     

中国粮食增产的主要因素贡献分解与实证估算

已有测算中国粮食增产要素贡献率的方法存在虚增或低估某要素贡献率的缺陷,文章通过改进粮食增产贡献率测算方法实证估计了2003—2016年期间单产、播种面积和结构调整对中国粮食增产的贡献。结果表明,中国粮食实现“十二连增”的巨大成就主要是单产增长贡献,年均贡献率50%以上,播种面积和作物内部结构调整也是不可忽视的重要力量,年均贡献率分别为30%与10%以上。然而,未来中国粮食增产依靠播种面积扩大和结构调整的空间变窄,需要探索新的增产路径。     

Mixture inverse Gaussian for unobserved heterogeneity in the autoregressive conditional duration model

In this paper, we assume that the duration of a process has two different intrinsic components or phases which are independent. The first is the time it takes for a trade to be initiated in the market (for example, the time during which agents obtain knowledge about the market in which they are operating and accumulate information, which is coherent with Brownian motion) and the second is the subsequent time required for the trade to develop into a complete duration. Of course, if the first time is zero then the trade is initiated immediately and no initial knowledge is required. If we assume a specific compound Bernoulli distribution for the first time and an inverse Gaussian distribution for the second, the resulting convolution model has a mixture of an inverse Gaussian distribution with its reciprocal, which allows us to specify and test the unobserved heterogeneity in the autoregressive conditional duration (ACD) model.

Our proposals make it possible not only to capture various density shapes of the durations but also easily to accommodate the behaviour of the tail of the distribution and the non monotonic hazard function. The proposed model is easy to fit and characterizes the behaviour of the conditional durations reasonably well in terms of statistical criteria based on point and density forecasts.     

企业财务危机预警双基点距离比值法

本文介绍了国内外主要企业财务危机预警模型,对模型的空间几何意义进行解释。提出了企业财务危机预警双基点距离比值模型,该模型采用非线性方法对危机企业与非危机企业的财务指标集合进行分割。用112例样本实证,预警识别率达到92.8%。     

基于可变参数优化的相关证据合成方法研究

总结了相关证据合成的理论方法,分析了已有合成方法的不足,对相关证据的相关焦元 的分布情况进行了分析,提出一种面向问题的基于可变参数的相关证据的合成方法. 方法只对 相关焦元的基本可信数进行修正,克服了对相关证据所有焦元进行修正的弱点,使修正的对象 更加合理,修正系数可用基于学习的寻优方法得到. 在相关证据的相关焦元的基本可信数未知 的情况下也可进行相关证据的合成. 计算结果表明该方法对合成的结果有明显的改进     

Improved communality estimation in factor analysis

The symmetric treatment of an asymmetric approach to factor analysis is shown to provide accurate communality estimates. In comparison with existing estimates including upper and lower bounds, the present approach is shown to be superior. In one example with known communalities, the present approach perfectly captures those communalities. In two empirical examples, it is shown to produce better communality estimates than any existing method.     

Estimating the probability of obtaining nonfeasible parameter estimates of the generalized pareto distribution

In this paper we consider the problem of estimating the parameters of the generalized Pareto distribution. Both the method of moments and probability-weighted moments do not guarantee that their respective estimates will be consistent with the observed data. We present simple programs to predict the probability of obtaining such nonfeasible estimates. Our estimation techniques are based on results from intensive simulations and the successful modelling of the lower tail of the distribution of the upper bound of the support. More simulations are performed to validate the new procedure.     

Two-stage hierarchical modeling for analysis of subpopulations in conditional distributions

In this work, we develop modeling and estimation approach for the analysis of cross-sectional clustered data with multimodal conditional distributions where the main interest is in analysis of subpopulations. It is proposed to model such data in a hierarchical model with conditional distributions viewed as finite mixtures of normal components. With a large number of observations in the lowest level clusters, a two-stage estimation approach is used. In the first stage, the normal mixture parameters in each lowest level cluster are estimated using robust methods. Robust alternatives to the maximum likelihood estimation are used to provide stable results even for data with conditional distributions such that their components may not quite meet normality assumptions. Then the lowest level cluster-specific means and standard deviations are modeled in a mixed effects model in the second stage. A small simulation study was conducted to compare performance of finite normal mixture population parameter estimates based on robust and maximum likelihood estimation in stage 1. The proposed modeling approach is illustrated through the analysis of mice tendon fibril diameters data. Analyses results address genotype differences between corresponding components in the mixtures and demonstrate advantages of robust estimation in stage 1.