广域网环境下构建全集成互动式学习平台

随着信息技术的不断发展,学习模式也应当是多方位的才能满足人才培养发展的需要。文章以CINOWO中文语言学习平台为例,分析如何建设一个广域网环境下的全集成互动式学习平台,既可以实现师生间多方位的教与学,也能达到更广泛程度的全民教育。     

安徽农村职业教育资源配置现状与优化途径

安徽农村职业教育是职业教育的主体,虽然近年来在困境中获得了较快的发展,取得了一些成就,但是,受人才与分配政策、城乡二元结构、地方经济、传统观念等因素的制约,农村职业教育在安徽省教育资源分配格局中一直处于弱势地位。＂建设职教大省＂,解决＂三农＂问题,促进经济社会、城乡一体化协调发展,公平合理、优化配置农村职业教育资源是实现人力资源强省的必然选择。     

半干旱地区农作物全膜覆盖双垄面集雨沟播试验示范研究

榆林素有十年九旱之称,是典型的雨养农业区,干旱依然是困扰农业发展的主要因素,目前≤15°的旱坡地有1058．1万亩,占全市耕地总资源的64．3％。全膜覆盖双垄面集雨沟播技术是适宜于在粮、油等经济效益高的作物上应用,却具有投资少,见效快、易操作等优点,将为榆林旱作农业发展开辟新途径。     

对中国各地区能源强度空间影响的实证研究

运用莫兰指数考察了地区能源强度的空间自相关性,并在能源要素禀赋差距指标和地理邻近矩阵的基础上创建了一个空间计量权重矩阵。通过Matlab7.0的空间计量工具箱,分别运用空间滞后面板数据模型和空间误差面板数据模型对中国地区间能源强度的空间效应进行了实证检验,结果证明了空间聚集效应的存在,邻近地区的能源强度确实会对目标地区的能源强度产生影响。     

ROC Curves in Non‐Parametric Location‐Scale Regression Models

Abstract. The receiver operating characteristic (ROC) curve is a tool of extensive use to analyse the discrimination capability of a diagnostic variable in medical studies. In certain situations, the presence of a covariate related to the diagnostic variable can increase the discriminating power of the ROC curve. In this article, we model the effect of the covariate over the diagnostic variable by means of non‐parametric location‐scale regression models. We propose a new non‐parametric estimator of the conditional ROC curve and study its asymptotic properties. We also present some simulations and an illustration to a data set concerning diagnosis of diabetes.     

丰水地区虚拟水投入产出分析——来自江西的实证

构建了区域水资源投入产出模型,通过对丰水地区的典型代表省份江西的农业、工业及其他产业虚拟水消费量、贸易量的计算,得出了各产业间虚拟水净转移以及区域间虚拟水的输入输出量,建立产业间虚拟水转移矩阵,量化了产业间虚拟水的转移情况。结果认为,农业是虚拟水最大的净转移和输出部门,其所利用的水量实际上向制造业大量转移。该结论从投入产出分析视角为丰水地区水资源管理提供了全新的政策建议。     

长株潭试验区行政管理体制改革研究

长株潭试验区行政管理体制改革是长株潭试验区两型社会建设的关键.为突破现行区划行政、城乡分治、功能过大、政绩导向、条条运行等体制性弊端,长株潭试验区行政管理体制要坚持渐进改革、效能优先、分层管理、创新、权威等改革原则,以准政府模式为目标选择,明晰区域行政管理组织的功能边界、合理配置区域行政管理组织财权、创新区域公共事务治理机制、区域集成发展的制度基础以及区域行政管理和服务技术.     

A Bayesian predictive inference for small area means incorporating covariates and sampling weights

The main goal in small area estimation is to use models to ‘borrow strength’ from the ensemble because the direct estimates of small area parameters are generally unreliable. However, model-based estimates from the small areas do not usually match the value of the single estimate for the large area. Benchmarking is done by applying a constraint, internally or externally, to ensure that the ‘total’ of the small areas matches the ‘grand total’. This is particularly useful because it is difficult to check model assumptions owing to the sparseness of the data. We use a Bayesian nested error regression model, which incorporates unit-level covariates and sampling weights, to develop a method to internally benchmark the finite population means of small areas. We use two examples to illustrate our method. We also perform a simulation study to further assess the properties of our method.     

ROBUST ESTIMATION OF SMALL‐AREA MEANS AND QUANTILES

Small‐area estimation techniques have typically relied on plug‐in estimation based on models containing random area effects. More recently, regression M‐quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the specification of random effects. However, the plug‐in M‐quantile estimator for the small‐area mean can be shown to be the expected value of this mean with respect to a generally biased estimator of the small‐area cumulative distribution function of the characteristic of interest. To correct this problem, we propose a general framework for robust small‐area estimation, based on representing a small‐area estimator as a functional of a predictor of this small‐area cumulative distribution function. Key advantages of this framework are that it naturally leads to integrated estimation of small‐area means and quantiles and is not restricted to M‐quantile models. We also discuss mean squared error estimation for the resulting estimators, and demonstrate the advantages of our approach through model‐based and design‐based simulations, with the latter using economic data collected in an Australian farm survey.     

Optimal Composite Markers for Time‐Dependent Receiver Operating Characteristic Curves with Censored Survival Data

Abstract. To increase the predictive abilities of several plasma biomarkers on the coronary artery disease (CAD)‐related vital statuses over time, our research interest mainly focuses on seeking combinations of these biomarkers with the highest time‐dependent receiver operating characteristic curves. An extended generalized linear model (EGLM) with time‐varying coefficients and an unknown bivariate link function is used to characterize the conditional distribution of time to CAD‐related death. Based on censored survival data, two non‐parametric procedures are proposed to estimate the optimal composite markers, linear predictors in the EGLM model. Estimation methods for the classification accuracies of the optimal composite markers are also proposed. In the article we establish theoretical results of the estimators and examine the corresponding finite‐sample properties through a series of simulations with different sample sizes, censoring rates and censoring mechanisms. Our optimization procedures and estimators are further shown to be useful through an application to a prospective cohort study of patients undergoing angiography.