从语篇变量看高考英语阅读测试的效度

阅读是最为重要的一项语言能力。在我国,阅读一直是中学英语教学的重点,在高考英语测试中也是最重要的部分之一。从语篇变量的角度建立了一套高考英语阅读测试的效度验证标准,并用该标准对2009年高考英语重庆卷阅读测试部分进行效度检验。结果表明,高考英语重庆卷阅读测试符合考试大纲和教学大纲的要求,设计科学、合理,知识覆盖全面,考查重点突出,具有较高的内容效度,但同时也存在一些不足。最后,为阅读教学和测试提出了相应的建议。     

水稻种植户生产性资金投入行为及其影响因素——以黑龙江佳木斯地区为例

采用对黑龙江佳木斯市桦南县和桦川县305户农户2009年全年水稻种植投入产出的调查数据,实证分析了水稻种植户生产性资金投入行为及其影响因素。运用Logit模型对固定生产投资可能性的影响因素进行了计量检验,结果表明:家庭规模、非农就业比例、家庭平均教育水平、耕地面积、家庭经营收入、信贷水平、农户对稻米最低收购价格期望度对固定生产性投资有显著影响;是否发生灾害对农户生产性投资规模无显著影响。运用Tobit模型对生产性资金投入规模的影响因素进行了计量检验,结果表明:家庭规模、非农就业比例、家庭平均教育水平、耕地面积、家庭经营收入、信贷水平和家庭住房价值、农户对稻米最低收购价格期望度、是否发生灾害对农户生产性投资规模有显著影响。在模型检验结果的基础上,提出促进水稻种植户增加固定生产性投资和增加生产性资金投入的合理建议:提高稻米最低收购价格;提高水稻种植的规模化程度;努力提高水稻种植户的教育水平;确保水稻种植户能够及时获得生产性资金贷款。     

若干可积的二维变系数线性微分系统

本文谈二维变系数线性微分系统的若干可积类,且指出求它们的通解的具体步骤。     

我国农民生育行为的社会结构解释

通过分析“成本-效用”的经济因素及文化因素解释说,对于农民非要生育男孩这一生育行为的解释在理论与方法论方面存在的局限性,提出了使用社会结构的分析视角来理清各影响因素彼此间的关系,即从内生变量因素(农村家庭养老、家庭结构和社区结构)以及外生变量因素(户籍制度、计划生育政策)2个方面对农民偏好男孩这一生育行为作出分析和解释。最终认为,理解并认可农民想要一个男孩是一件并不过分的事,这有助于促进人口工作取得实际成效。     

基于熵权法的财务危机预警指标选择研究

合理选择财务指标是财务危机预警研究中的重要内容,用定性和定量相结合的方法进行选择是科学有效的方法之一。首先根据初选原则对财务指标进行了初步选择,然后提出了一种基于熵权的变量选择方法,对初选的财务指标进行定量筛选,进行实证研究,最终确定了应用于财务危机预警的财务指标。     

随机变量的平均不等式(Ⅰ)

文献[1]定义了随机变量的算术平均与几何平均,并建立了对称随机变量的平均不等式。本文借助于[1]的定义与方法,建立了更为一般的算术平均、几何平均、期望不等式。并将 Diaz—Metcalf 不等式、Plya—Szeg不等式、Kantorovich 不等式作为推论导出。利用本文所建立的不等式在一定条件下还可以用来估计方差的上界。     

Bayesian Model Choice in Exponential Survival Models

ABSTRACT

Log-linear models for the distribution on a contingency table are represented as the intersection of only two kinds of log-linear models. One assuming that a certain group of the variables, if conditioned on all other variables, has a jointly independent distribution and another one assuming that a certain group of the variables, if conditioned on all other variables, has no highest order interaction. The subsets entering into these models are uniquely determined by the original log-linear model. This canonical representation suggests considering joint conditional independence and conditional no highest order association as the elementary building blocks of log-linear models.     

Bayesian ikference procedures derived via the concept of relative surprise

We consider the problem of deriving Bayesian inference procedures via the concept of relative surprise. The mathematical concept of surprise has been developed by I.J. Good in a long sequence of papers. We make a modification to this development that permits the avoidance of a serious defect; namely, the change of variable problem. We apply relative surprise to the development of estimation, hypothesis testing and model checking procedures. Important advantages of the relative surprise approach to inference include the lack of dependence on a particular loss function and complete freedom to the statistician in the choice of prior for hypothesis testing problems. Links are established with common Bayesian inference procedures such as highest posterior density regions, modal estimates and Bayes factors. From a practical perspective new inference procedures arise that possess good properties.     

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online.     

Robust variable selection and parametric component identification in varying coefficient models

ABSTRACT

In this paper, we study a novelly robust variable selection and parametric component identification simultaneously in varying coefficient models. The proposed estimator is based on spline approximation and two smoothly clipped absolute deviation (SCAD) penalties through rank regression, which is robust with respect to heavy-tailed errors or outliers in the response. Furthermore, when the tuning parameter is chosen by modified BIC criterion, we show that the proposed procedure is consistent both in variable selection and the separation of varying and constant coefficients. In addition, the estimators of varying coefficients possess the optimal convergence rate under some assumptions, and the estimators of constant coefficients have the same asymptotic distribution as their counterparts obtained when the true model is known. Simulation studies and a real data example are undertaken to assess the finite sample performance of the proposed variable selection procedure.