RECURSIVE RESIDUALS FOR MULTIVARIATE REGRESSION MODELS

Recursive residuals and their relationship to the recursive estimation of regression parameters have been developed for unvaried regression mod els. Such residuals and estimates have been used to test the constancy of regression over time. The current paper extends this work to multivariate regression modal.     

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.     

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.     

ON THE CONVERGENCE RESULT FOR THE SUPERCRITICAL BELLMAN-HARRIS PROCESS1

It was proved in Cohn (1982) that for any finite offspring mean, supercritical Bellman-Harris process {Z(t)} there exist some norming constants {C(t)} such that {Z(t)/C(t)} converges almost surely to a non-degenerate random variable W. {C(t)} were defined to be the μ-quantiles of {Z(t)}. Schuh (1982) has given an alternative proof of this result, identifying C(t) as “the Seneta constants” 1/(-log F_t^(-1)(γ)), where F₁(γ) = E(γ^Z(t)). Both proofs are long and complicated. It will be shown here that a much simpler proof can be devised from Cohn (1982), if use is made of an elementarily proved property given in Schuh (1982).