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.     

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.     

Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms

Summary.  In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.     

西部高校怎样更好实现计算机与物理实验的整合

随着计算机的飞速发展,学校里出现了越来越多的多媒体教室;CAI计算机辅助教学、虚拟实验教学等各种软件更是蓬勃发展起来;很多教育工作者提出物理实验中计算机仿真教学、物理实验中应用计算机辅助教学等各种措施.作为西部高校,我们应该根据自身资金有限、学生基础比较薄弱等特点,更好的实现计算机与物理实验的整合.     

Influence diagnostics and outlier tests for semiparametric mixed models

Summary. Semiparametric mixed models are useful in biometric and econometric applications, especially for longitudinal data. Maximum penalized likelihood estimators (MPLEs) have been shown to work well by Zhang and co-workers for both linear coefficients and nonparametric functions. This paper considers the role of influence diagnostics in the MPLE by extending the case deletion and subject deletion analysis of linear models to accommodate the inclusion of a nonparametric component. We focus on influence measures for the fixed effects and provide formulae that are analogous to those for simpler models and readily computable with the MPLE algorithm. We also establish an equivalence between the case or subject deletion model and a mean shift outlier model from which we derive tests for outliers. The influence diagnostics proposed are illustrated through a longitudinal hormone study on progesterone and a simulated example.     

数据挖掘与数据仓库在CRM中的应用

介绍了客户关系管理的思想、功能组成 ,以及数据挖掘和数据仓库技术在客户关系管理中的应用