Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality

Spatially explicit data pose a series of opportunities and challenges for all the actors involved in providing data for long-term preservation and secondary analysis—the data producer, the data archive, and the data user. We report on opportunities and challenges for each of the three players, and then turn to a summary of current thinking about how best to prepare, archive, disseminate, and make use of social science data that have spatially explicit identification. The core issue that runs through the paper is the risk of the disclosure of the identity of respondents. If we know where they live, where they work, or where they own property, it is possible to find out who they are. Those involved in collecting, archiving, and using data need to be aware of the risks of disclosure and become familiar with best practices to avoid disclosures that will be harmful to respondents.

《中国廿六史及明清实录东亚关系史料全辑》的编纂综述及史料价值

中韩两国合作项目《中国廿六史及明清实录东亚三国关系史料全辑》,是介绍古代中国、朝鲜(韩国)、日本三国关系的大型史料工具书,它的出版,具有重大的学术价值和深远的现实意义.     

出口贸易与省域TFP空间维度分析

作为衡量一国科技进步的重要指标,全要素生产率的高低决定着一国生产率水平的先进与落后。在不断深化改革开放的过程中,如何利用开放倒逼我国技术水平的提高成为一项较为紧迫的任务。为此,我们运用面板DEA-Malmquist指数法可以测算出2002—2011年我国29个省份(含自治区、直辖市)全要素生产率,并在此基础上建立空间面板数据模型(SPDM),实证分析出口贸易、基础设施等对我国省域全要素生产率及其空间溢出效应的影响。经研究发现,我国省域全要素生产率存在较为明显的空间自相关性,即出口贸易、基础设施等经济活动通过影响本省TFP,并通过空间溢出效应进一步将其作用传递和迭加到邻近省域全要素生产率上,基础设施对我国省域全要素存在较为显著的负向关系,出口依存度、出口贸易及FDI对我国省域全要素生产率具有显著的推动作用。     

我国中小零售企业物流系统运营效率研究

近年来我国中小零售企业发展快速且竞争激烈,零售物流系统是高成本时代中小零售企业发展的关键。运用数据包络分析(DEA)法,对比分析18家中小零售企业的物流系统经营数据可知,中小零售企业物流作业要树立效率经营理念,不断加强物流系统内部管理,从而建设高效零售物流体系,提升企业运营效率。     

统计学专业本科生开设“数据挖掘”课程的探讨

"数据挖掘"是一门分析海量数据库的交叉学科,统计学专业开设这门课程十分必要。鉴于数据挖掘内容的复杂多样,本文提出应根据统计学专业本科生的特点制订合理的教学大纲、教学内容和教学方法,以提高学生的学习效率和学习热情。     

Generalized empirical likelihood inference in partial linear regression model for longitudinal data

In this paper, empirical likelihood inference for longitudinal data within the framework of partial linear regression models are investigated. The proposed procedures take into consideration the correlation within groups without involving direct estimation of nuisance parameters in the correlation matrix. The empirical likelihood method is used to estimate the regression coefficients and the baseline function, and to construct confidence intervals. A nonparametric version of Wilk's theorem for the limiting distribution of the empirical likelihood ratio is derived. Compared with methods based on normal approximations, the empirical likelihood does not require consistent estimators for the asymptotic variance and bias. The finite sample behaviour of the proposed method is evaluated with simulation and illustrated with an AIDS clinical trial data set.     

Testing for heteroscedasticity of exponential correlation mixed-effects linear models based on M-estimation

Homogeneity of variance is a basic assumption in longitudinal data analysis. However, the assumption is not necessarily appropriate. In this paper, Fisher scoring method is applied to get M-estimator in the exponential correlation mixed-effects linear model. The score tests for heteroscedasticity and correlation coefficient based on M-estimator are then studied. Monte Carlo method is applied to investigate the properties of test statistics. At last, the methods and properties are illustrated by an actual data example.     

Testing Treatment-Covariate Interaction in a Nonparametric Analysis of Covariance Model

We consider a recently introduced nonparametric model for Analysis of Covariance and derive an asymptotic test for interaction between covariate and treatment. Furthermore, we suggest data depth techniques to obtain joint confidence regions for the covariate effects in this model.

The finite sample behavior of the asymptotic method is evaluated in simulations. Application of the procedures is illustrated using an epileptic seizures and chemotherapy data set.     

A Critical Look at Some Analyses of Major League Baseball Salaries

At a data analysis exposition sponsored by the Section on Statistical Graphics of the ASA in 1988, 15 groups of statisticians analyzed the same data about salaries of major league baseball players. By examining what they did, what worked, and what failed, we can begin to learn about the relative strengths and weaknesses of different approaches to analyzing data. The data are rich in difficulties. They require reexpression, contain errors and outliers, and exhibit nonlinear relationships. They thus pose a realistic challenge to the variety of data analysis techniques used. The analysis groups chose a wide range of model-fitting methods, including regression, principal components, factor analysis, time series, and CART. We thus have an effective framework for comparing these approaches so that we can learn more about them. Our examination shows that approaches commonly identified with Exploratory Data Analysis are substantially more effective at revealing the underlying patterns in the data and at building parsimonious, understandable models that fit the data well. We also find that common data displays, when applied carefully, are often sufficient for even complex analyses such as this.