Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

基于移动代理的新型IDS维护更新机制设计

提出了一种基于移动代理的新型分布式入侵检测系统。该系统是针对广域网环境专门设计的,数据的处理通过各节点所设置的代理来进行分布式计算,不仅能实现全网络范围内的入侵检测功能,具有良好的可移植性;而且对网络系统和主机的资源占用较低,减少了出现网络瓶颈的可能。还建立了移动代理的新型分布式入侵检测系统的体系结构和理论分析模型,并讨论了该系统的维护更新机制。     

Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

A note on interval‐censored lifetime data and the constant‐sum condition of oiler,gómez & calle (2004)

Oiler, Gomez & Calle (2004) give a constant sum condition for processes that generate interval‐censored lifetime data. They show that in models satisfying this condition, it is possible to estimate non‐parametrically the lifetime distribution based on a well‐known simplified likelihood. The author shows that this constant‐sum condition is equivalent to the existence of an observation process that is independent of lifetimes and which gives the same probability distribution for the observed data as the underlying true process.     

Temporal pattern in number of staff on sick leave: the effect of downsizing

Summary.  The pattern of absenteeism in the downsizing process of companies is a topic in focus in economics and social science. A general question is whether employees who are frequently absent are more likely to be selected to be laid off or in contrast whether employees to be dismissed are more likely to be absent for the remaining time of their working contract. We pursue an empirical and microeconomic investigation of these theses. We analyse longitudinal data that were collected in a German company over several years. We fit a semiparametric transition model based on a mixture Poisson distribution for the days of absenteeism per month. Prediction intervals are considered and the primary focus is on the period of downsizing. The data reveal clear evidence for the hypothesis that employees who are to be laid off are more frequently absent before leaving the company. Interestingly, though, no clear evidence is seen that employees being selected to leave the company are those with a bad absenteeism profile.     

浅议高职学生心理素质教育

由于自身的特点 ,当代大学生面对社会变革和竞争压力 ,产生了诸多的心理问题乃至心理疾病。而高职学生作为其中的一个特殊群体 ,存在的心理问题更为突出。基于大学生心理素质教育的现实 ,本文分析了高职学生存在的心理问题及产生的原因 ,并有针对性地提出了解决的方法和途径。     

谈我市国有企业改革的现状与对策

深化国有企业改革是推进我国经济体制改革发展的重要组成部分。我市国有企业存在着体制改革形式过于单一、企业运作不规范、资金短缺、负担过重等问题。为了进一步深化国有企业改革,政府应把现代企业制度落到实处,努力实现投资主体的多元化,切实做到政企分开,着力解决再就业工程,为企业的健康发展提供良好的经济环境。     

A theoretical basis for perturbation methods

In this paper we discuss a new theoretical basis for perturbation methods. In developing this new theoretical basis, we define the ideal measures of data utility and disclosure risk. Maximum data utility is achieved when the statistical characteristics of the perturbed data are the same as that of the original data. Disclosure risk is minimized if providing users with microdata access does not result in any additional information. We show that when the perturbed values of the confidential variables are generated as independent realizations from the distribution of the confidential variables conditioned on the non-confidential variables, they satisfy the data utility and disclosure risk requirements. We also discuss the relationship between the theoretical basis and some commonly used methods for generating perturbed values of confidential numerical variables.     

Local spatial data infrastructure, Trujillo-Peru

The development of a Municipal Information System, or currently better known as a local spatial data infrastructure, is considered complex due to the required inter-institutional relationships. In many developing countries Geographical Information Systems (GISs) are introduced but the benefits are modest as no changes take place in technical and organisational structure of organisations. Digital databases and computer-aided design (CAD) maps are mushrooming in great variety within different private and public institutions, municipal organisations and even within single departments and with structures similar to the paper period and thus operating on a stand-alone basis.Many national mapping agencies are not able to provide large-scale digital urban base maps, while the absence or low quality of cadastres makes those basic core data sets unavailable or inaccessible. The result is that duplication and incompatible data are frequently observed and also donor-driven stand-alone projects have a limited impact through the lack of institutional embedding and are not able to mature from the project to the institutional level. However, a positive sign is that there is an increasing awareness among data producers and consumers that investments in the development of digital data sets should be combined to reduce costs and increase benefits from especially GIS, and information and communication technology (ICT) in general.Within Trujillo a long-term vision was developed to make full use of ICT and GIS to modernise all operations of the Municipality to increase the efficiency and effectiveness of its tasks. However, large investments are not feasible due to the very limited municipal budgets. To guarantee the support of the municipal council, short-term results are required. This paper describes three ‘products’ as part of the vision to develop through a step-by-step approach a local spatial data infrastructure for Trujillo.The three, rather different, products are:

1. fiscal cadastre, to increase municipal revenues through property taxation;

2. an ‘environmental atlas’ based on a compatible spatial and attribute data sets from a variety of organisations; and

3. a municipal website with interactive GIS and meta data information.

The paper concludes with some comments on the institutional and political requirements for the successful development of a local spatial data infrastructure and stresses that such an infrastructure is not a product but an incremental development process that will progress only in case such a process is simple, cost effective, user-friendly, and flexible with clear products.Political interest and institutional stability and genuine interest for inter-institutional cooperation will add much to the success of this process.     

Multiple imputation of missing data with ante-dependence covariance structure

A controlled clinical trial was conducted to investigate the efficacy effect of a chemical compound in the treatment of Premenstrual Dysphoric Disorder (PMDD). The data from the trial showed a non-monotone pattern of missing data and an ante-dependence covariance structure. A new analytical method for imputing the missing data with the ante-dependence covariance is proposed. The PMDD data are analysed by the non-imputation method and two imputation methods: the proposed method and the MCMC method.