Limit theorems for a general weighted process under random censoring

Necessary and sufficient conditions for weak and strong convergence are derived for the weighted version of a general process under random censoring. To be more explicit, this means that for this process complete analogues are obtained of the Chibisov-O'Reilly theorem, the Lai-Wellner Glivenko-Cantelli theorem, and the James law of the iterated logarithm for the empirical process. The process contains as special cases the so-called basic martingale, the empirical cumulative hazard process, and the product-limit process. As a tool we derive a Kiefer-process-type approximation of our process, which may be of independent interest.     

Population policies in advanced societies: Pronatalist and migration strategies

This paper discusses pronatalist and migration policies that have been introduced in selected European countries. Measures and objectives are briefly described before an evaluation of the efficacy of each is provided. The case studies chosen show that pronatalist policies have had limited effects. Migration policies seem to have been more effective, although their aims so far have been largely non-demographic. Migration strategies will increasingly have to be considered for demographic reasons in the future, to counterbalance population decline. Both pronatalist and migration strategies tend to conflict with other policies (social and economic policies, emancipation or housing policies), and to be in competition with the demands of an ageing society. Limits to the development or expansion of either strategy are spelled out. Finally a few untried mechanisms are mentioned.     

EXPLORA:content interpretation of data

Most approaches to applying knowledge-based techniques for data analyses concentrate on the context-independent statistical support. EXPLORA however is developed for the subject-specific interpretation with regard to the contents of the data to be analyzed (i.e. content interpretation). Therefore its knowledge base includes also the objects and semantic relations of the real system that produces the data. In this paper we describe the functional model representing the process of content interpretation, summarize the software architecture of the system and give some examples of its applications by pilot-users in survey analysis. EXPLORA addresses applications with data produced regularly which have to be analyzed in a routine way. The system systematically searches for statistical results (facts) to detect relations which possibly could be overlooked by a human analyst. On the other hand EXPLORA will help overcome the large bulk of information which today is usually still produced when presenting the data. Therefore a second knowledge process of content interpretation consists in discovering messages about the data by condensing the facts. Approaches for inductive generalization which have been developed for machine learning are utilized to identify common values of attributes of the objects to which the facts relate. At a later stage the system searches for interesting facts by applying redundancy rules and domaindependent selection rules. EXPLORA formulates the messages in terms of the domain, groups and orders them and even provides flexible navigations in the fact spaces.     

Automatic selection of the proper family for simultaneous confidence intervals

Statisticians often employ simultaneous confidence intervals to reduce the likelihood of their drawing false conclusions when they must make a number of comparisons. To do this properly, it is necessary to consider the family of comparisons over which simultaneous confidence must be assured. Sometimes it is not clear what family of comparisons is appropriate. We describe how computer software can monitor the types of contrasts a user examines, and select the smallest family of contrasts that is likely to be of interest. We also describe how to calculate simultaneous confidence intervals for these families using a hybrid of the Bonferroni and Scheffé methods. Our method is especially suitable for problems with discrete and continuous predictors.     

Cost containment in health care: a model for management research.

Cost containment is a dominant problem in the health care field, but it has not been addressed from a comprehensive management perspective. To fill this gap, we have developed an inclusive model of the cost containment process. The model has implications for management research in several areas: cost containment baselines, incentive systems, organization structures, cost/quality trade-offs, and cost containment constraints.