Non-asymptotic sequential confidence regions with fixed sizes for the multivariate nonlinear parameters of regression

In this paper we consider a sequential design for the estimation of nonlinear parameters of regression with guaranteed accuracy. Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used. The obtained confidence region is valid for finite numbers of data points when the distributions of the observations are unknown.     

Estimation and Inference Procedures for Semiparametric Distribution Models with Varying Linear‐Index

More flexible semiparametric linear‐index regression models are proposed to describe the conditional distribution. Such a model formulation captures varying effects of covariates over the support of a response variable in distribution, offers an alternative perspective on dimension reduction and covers a lot of widely used parametric and semiparameteric regression models. A feasible pseudo likelihood approach, accompanied with a simple and easily implemented algorithm, is further developed for the mixed case with both varying and invariant coefficients. By justifying some theoretical properties on Banach spaces, the uniform consistency and asymptotic Gaussian process of the proposed estimator are also established in this article. In addition, under the monotonicity of distribution in linear‐index, we develop an alternative approach based on maximizing a varying accuracy measure. By virtue of the asymptotic recursion relation for the estimators, some of the achievements in this direction include showing the convergence of the iterative computation procedure and establishing the large sample properties of the resulting estimator. It is noticeable that our theoretical framework is very helpful in constructing confidence bands for the parameters of interest and tests for the hypotheses of various qualitative structures in distribution. Generally, the developed estimation and inference procedures perform quite satisfactorily in the conducted simulations and are demonstrated to be useful in reanalysing data from the Boston house price study and the World Values Survey.     

Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions

The forecasting of sales in a company is one of the crucial challenges that must be faced. Nowadays, there is a large spectrum of methods that enable making reliable forecasts. However, sometimes the nature of time series excludes many well-known and widely used forecasting methods (e.g., econometric models). Therefore, the authors decided to forecast on the basis of a seasonally adjusted median of selected probability distributions. The obtained forecasts were verified by means of distributions of the Theil U² coefficient and unbiasedness coefficient.     

桑弘羊经济立法思想探微

西汉中期,时为掌管封建中央财政事务大臣的桑弘羊在坚持先秦法家思想的基础上,先后推行和提出了大力发展官商,打击私商,实施盐铁专卖,均输平准等一系列的经济立法措施和财政政策,它是汉王朝社会经济方式由自由放任转向国家干预控制的重要转折,其思想对加强封建中央财政管理,充实封建国家财政收入和缓解社会财政危机发挥了重要的作用,其许多首创的理论和实践被后代各封建王朝的理财大臣看作是可望不可及的成功的财政典型,且为当时世界上不少国家所推崇和采用。研究这一中国古代法制史上的重要成果,对思考我们现实社会财政经济的改革发展有宝贵的借鉴意义。     

众包标注质量控制研究

众包是近年来流行的一种分布式商业模式,采用众包方式获取标注是一种快速廉价获取海量数据标签的有效方式,由于众包平台中普遍存在不认真完成任务的作弊用户,众包标注质量难以保证.为有效提高众包标注准确性,通过学习标注者的可靠性,实现基于标注者的可靠性进行动态任务分配,采用贪婪的预测模型实现对多个标注者提交的标签进行聚集,获得高质量的众包标注结果.实验结果表明,该方法能获得比基准算法更高的标注准确性,有效实现众包标注质量控制.     

以知识管理解决政府信息失真问题的对策与建议

解决政府信息失真的问题,是提高政府管理能力和管理效率的重要途径。要从根本上真正解决政府信息失真的问题,光靠技术上的信息管理是不够的。文章指出运用知识管理理论来解决政府信息失真问题可能是一条有效途径,运用知识管理理论,建立起学习型政府,明确以知识管理解决政府信息问题的原则,并采取一系列切实有效的对策和措施从根本上解决政府信息失真。     

中国文化推广的战略定位和实施路径

中国文化推广缺乏学术研究的引领和文化产业的支撑。中国文化推广的战略定位和实施路径应环绕三个枢纽展开：中国文化推广现状,中国文化推广路径,中国文化推广体系;着力解决好四个关键：文化推广路径调研,传统文化精品尤其是哲理思想和世界性理论问题互动,（多模态）影像片创意和精美制作,发展文化产业。只有这样,中国文化推广的春天甚至是夏天才会到来。     

我国公民之法治信仰

在对法治信仰作出理论探讨的基础上，就我国公民法治信仰进行了历史考查与现实定位，并以全新的视角，提出了推动公民法治信仰的形成，巩固和提高的思路。     

Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model

Electricity consumption forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may cause wastes of scarce energy resource or electricity shortages. However, forecasting electricity consumption has proven to be a challenging task due to various unstable factors. Especially, China is undergoing a period of economic transition, which highlights this difficulty. This paper proposes a time-varying-weight combining method, i.e. High-order Markov chain based Time-varying Weighted Average (HM-TWA) method to predict the monthly electricity consumption in China. HM-TWA first calculates the in-sample time-varying combining weights by quadratic programming for the individual forecasts. Then it predicts the out-of-sample time-varying adaptive weights through extrapolating these in-sample weights using a high-order Markov chain model. Finally, the combined forecasts can be obtained. In addition, to ensure that the sample data have the same properties as the required forecasts, a reasonable multi-step-ahead forecasting scheme is designed for HM-TWA. The out-of-sample forecasting performance evaluation shows that HM-TWA outperforms the component models and traditional combining methods, and its effectiveness is further verified by comparing it with some other existing models.     

高职教育的观念重塑与体系重构

高职教育应当以培养学生的职业技能为核心，以不断提高毕业生的就业竞争力为依归。因此，高职院校应当体现以人为本、以学生为本的办学理念，在就读与就业之间建立起绿色通道；应当将学生职业生涯规划纳入教育视野，构建以职业生涯规划教育为中心的新型高职教育体系。职业生涯规划教育不应当仅作为就业指导的一项内容，而应当将其与高职教育人才培养模式的改革紧密联系。通过建立以职业生涯规划为中心的新型高职教育体系，围绕学生职业生涯规划来重构教学内容和进行教学改革，并将其贯穿于高职教育的全过程。