腐败的主观测评方法——以腐败感知指数为例

腐败测评是反腐败研究的重要组成部分,中国的腐败测评体系亟待发展。腐败测评有主观测评法和客观测评法两种研究思路。从统计机理和统计应用情况看,腐败的主观测评方法处于腐败测评研究的主流地位。本文介绍了腐败主观测评方法的基本思路、研究进展,并以腐败感知指数为例对其理论与应用情况进行了说明。     

区域主导产业评价指标与数学模型

以现有研究成果为基础,建立了区域主导产业评价指标体系;基于灰色聚类的思想,提出了评价区域主导产业优度的数学模型-定权聚类评估模型,并对河南省武陟县工业主导产业选择进行了实证研究。     

The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology

For frequency counts, the situation of extra zeros often arises in biomedical applications. This is demonstrated with count data from a dental epidemiological study in Belo Horizonte (the Belo Horizonte caries prevention study) which evaluated various programmes for reducing caries. Extra zeros, however, violate the variance–mean relationship of the Poisson error structure. This extra-Poisson variation can easily be explained by a special mixture model, the zero-inflated Poisson (ZIP) model. On the basis of the ZIP model, a graphical device is presented which not only summarizes the mixing distribution but also provides visual information about the overall mean. This device can be exploited to evaluate and compare various groups. Ways are discussed to include covariates and to develop an extension of the conventional Poisson regression. Finally, a method to evaluate intervention effects on the basis of the ZIP regression model is described and applied to the data of the Belo Horizonte caries prevention study.     

民族素质是构成现代发展概念的重要内涵--民族素质研究之五

本文从与发展内涵相关的人类发展的诸项指数、国家实力概念的探讨情况,论述了民族素质在现代发展概念中的重要地位.     

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。