Studying Complexity of Model-based Clustering

Cluster analysis is a popular statistics and computer science technique commonly used in various areas of research. In this article, we investigate factors that can influence clustering performance in the model-based clustering framework. The four factors considered are the level of overlap, number of clusters, number of dimensions, and sample size. Through a comprehensive simulation study, we investigate model-based clustering in different settings. As a measure of clustering performance, we employ three popular classification indices capable of reflecting the degree of agreement in two partitioning vectors, thus making the comparison between the true and estimated classification vectors possible. In addition to studying clustering complexity, the performance of the three classification measures is evaluated.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.     

Functional Partial Linear Single‐index Model

This paper deals with the problem of predicting the real‐valued response variable using explanatory variables containing both multivariate random variable and random curve. The proposed functional partial linear single‐index model treats the multivariate random variable as linear part and the random curve as functional single‐index part, respectively. To estimate the non‐parametric link function, the functional single‐index and the parameters in the linear part, a two‐stage estimation procedure is proposed. Compared with existing semi‐parametric methods, the proposed approach requires no initial estimation and iteration. Asymptotical properties are established for both the parameters in the linear part and the functional single‐index. The convergence rate for the non‐parametric link function is also given. In addition, asymptotical normality of the error variance is obtained that facilitates the construction of confidence region and hypothesis testing for the unknown parameter. Numerical experiments including simulation studies and a real‐data analysis are conducted to evaluate the empirical performance of the proposed method.     

煤炭大数据指数编制及经验模态分解模型研究

基于开放性数据源、连续观测昨多变量数据编制的大数据指数,与传统的统计调查指数存在的差异不仅在于数据本身的无限扩张,而且在于编制方法以及分解研究的规则、模型方面的差异。在大数据背景下,率先尝试性地提出大数据指数的定义和数据假设,将"互联网大数据指数"引入煤炭交易价格指数综合编制太原煤炭交易大数据指数,从而反映煤炭价格的变动趋势;导入经验模态分解模型,对所编制的煤炭大数据指数进行分解研究,尝试比较与传统的统计调查指数的差异。研究表明:新编制的煤炭价格大数据指数要比太原煤炭交易价格指数更为敏感和迅速,能更好地反映煤炭价格的变动趋势。随着"互联网+"和大数据战略的逐渐普及,基于互联网大数据编制的综合指数会影响到更多领域,将成为经济管理和社会发展各个领域的晴雨表和指示器;与传统统计调查指数逐步融合、互补或者升级,成为宏观经济大数据指数的重要组成部分。     

中印贸易的竞争性与互补性研究

近年来中印双边贸易发展迅速.本文在描述中印双边贸易发展现状的基础上,通过贸易强度指数、产业内贸易指数、贸易特化系数、出口相似度指数、显性比较优势指数五个指标研究了中印贸易竞争性和互补性,揭示中印两国的比较优势并总结中印贸易的特征.结果显示,中印贸易结构有一定的竞争性,但也有很大的互补性.     

上证180指数效应实证研究

选取上证180指数2002年12月到2005年1月期间五次成份股调整为样本,采用标准化残差方法 克服了事件研究中因聚类问题对统计结果的影响,对指数调整期间的价格和成交量效应进行 实证研究。研究发现,调入和调出股票均对调整信息做出了相应的反应,调入股票存在正的 持久的超额收益,调出股票负的超额收益发生了反转。这种非对称现象可以由投资者关注和 市场分割假说解释     

学位与研究生教育自我评估指标体系的研究

本文针对国内学位与研究生教育领域发展中存在着国内高校学科自我评估体系尚不健全等问题,对学位与研究生教育学科自我评估系统进行了研究,并结合工科院校的特点和实际情况,研究工科院校学科建设管理及自我评估工作,在理论研究、现状调研和专家走访的基础上,依托原有评估指标体系提出自我评估指标体系意见。     

我国区域不均等:分解方法与实证分析

在对区域不均等度量指标及分解方法进行归纳性阐释的基础上,运用泰尔指数分析了我国自20世纪80年代中后期以来的区域不均等,并对我国区域差异中的城乡收入差距,东中西部农村地区的收入差距进行了分解。结果表明,在总的不均等中约70-80%的不均等来自城乡之间,只有20%-30%是由城市和农村内部引起的。东中西之间的不均等对总不均等的贡献大约只有20-30%,剩下的70-80%则归因于东中西各自内部不同省份之间的差距。     

本科院校课程建设评价指标体系研究

课程建设水平的高低直接影响着高校教学质量的高低和所培养人才的情况.而课程建设评价指标设立的正确与否,则成为正确评价课程建设水平的