Influence analysis based on the case sensitivity function

The case sensitivity function approach to influence analysis is introduced as a natural smooth extension of influence curve methodology in which both the insights of geometry and the power of (convex) analysis are available. In it, perturbation is defined as movement between probability vectors defining weighted empirical distributions. A Euclidean geometry is proposed giving such perturbations both size and direction. The notion of the salience of a perturbation is emphasized. This approach has several benefits. A general probability case weight analysis results. Answers to a number of outstanding questions follow directly. Rescaled versions of the three usual finite sample influence curve measures—seen now to be required for comparability across different-sized subsets of cases—are readily available. These new diagnostics directly measure the salience of the (infinitesimal) perturbations involved. Their essential unity, both within and between subsets, is evident geometrically. Finally it is shown how a relaxation strategy, in which a high dimensional ( O ( ⁿC_m )) discrete problem is replaced by a low dimensional ( O ( n )) continuous problem, can combine with (convex) optimization results to deliver better performance in challenging multiple-case influence problems. Further developments are briefly indicated.     

WRAPAROUND OR INDIVIDUALISED MODELS OF CARE: DO THEY FIT INTO AUSTRALIAN CHILD WELFARE?

In Australia there is some interest in wraparound or individualised models of care as an alternative to traditional modes of child welfare funding and service delivery. This article points to the origin and core principles that guide attempts to create this post-institutional pattern of service. Following this a definition of wraparound or individualised services is offered. The current state of US research into the effectiveness of services provided under this rubric is then reported. Finally, comment is offered about the relevance of wraparound and individualised forms of service for Australian child welfare.     

Efficient and easy-to-use sample size formulas in ratio-based non-inferiority tests

In many biomedical applications, tests for the classical hypotheses based on the difference of treatment means in a one-way layout can be replaced by tests for ratios (or tests for relative changes). This approach is well noted for its simplicity in defining the margins, as for example in tests for non-inferiority. Here, we derive approximate and efficient sample size formulas in a multiple testing situation and then thoroughly investigate the relative performance of hypothesis testing based on the ratios of treatment means when compared with differences of means. The results will be illustrated with an example on simultaneous tests for non-inferiority.     

THE DEMAND for ILLICIT DRUGS

This paper estimates the effects of alcohol prices, marijuana decriminalization, cocaine prices, and heroin prices on the demand for these four substances. Both own price effects and cross price effects are estimated. The estimated price elasticities for alcohol, cocaine, and heroin are, respectively, –.30, –.28 and –.94. Marijuana decriminalization was found to increase the probability of marijuana participation by about 8%. The results for the cross price effects provide general evidence of complementarity. It is estimated that decriminalization of cocaine and heroin might lead to about 260,000 new regular cocaine users and about 47,000 new regular heroin users. ( JEL 110)     

Foster care assessment: an exploratory study of the placement assessment process in Flanders and the Netherlands

Family foster care placement decision‐making has a weak scientific underpinning. The identification of clusters of foster children (groups of foster children with similar characteristics) can help improve decision quality. In this study, we investigated if foster children could indeed be clustered, which problems were identified at the time of placement and what might be the influence of placement history. Two clusters of foster children were found: (i) young children with coinciding parental child‐rearing incapacity and familial problems, and (ii) older children with child problems. At the time of placement, familial problems were more often identified in younger children with a placement history. These findings stress the importance of approaching foster care assessment as part of a dynamic decision‐making process. It is key to finding the most appropriate situation for the child. At the same time, it must be decided how the desired situation can be realized, wherein placement decisions are based on an appraisal of whether or not a foster placement is an appropriate solution. In conjunction with this, it needs to be decided how the parents can be supported towards reunification, or whether or not long‐term foster care is the best option for the child, and if so what conditions need to be met.