On the <Emphasis Type="Italic">m</Emphasis>-clique free interval subgraphs polytope: polyhedral analysis and applications

In this paper we study the m-clique free interval subgraphs. We investigate the facial structure of the polytope defined as the convex hull of the incidence vectors associated with these subgraphs. We also present some facet-defining inequalities to strengthen the associated linear relaxation. As an application, the generalized open-shop problem with disjunctive constraints (GOSDC) is considered. Indeed, by a projection on a set of variables, the m-clique free interval subgraphs represent the solution of an integer linear program solving the GOSDC presented in this paper. Moreover, we propose exact and heuristic separation algorithms, which are exploited into a Branch-and-cut algorithm for solving the GOSDC. Finally, we present and discuss some computational results.     

Multidimensional Resilience: Predictive and Construct Validity of the Individual,Family, and Community Resilience Profile

Few measures parallel the robust depth offered in the existing multidimensional and ecologically informed theories of resilience. This study sought to evaluate the test–retest reliability, construct, and predictive validity of the individual, family, and community resilience resource profile (IFCR-R). The IFCR-R measures a family’s access to resilience resources and protective factors across multiple ecological levels. Confirmatory factor analysis was used with a sample of n?=?810 low-income families. And 159 families completed multiple time point measures for test–retest reliability and predictive validity evaluation. Results of this study support the proposed multidimensional ecological structure of the IFCR-R and found that the IFCR-R offers an acceptable test–retest reliability and predictive validity for outcomes of mental and physical health.     

Teachers’ Perceptions of Childcare and Preschool Expulsion

Previous research outlines the correlates of childcare expulsion in the USA, yet researchers know little about how these correlates produce expulsion. This in‐depth qualitative analysis of 30 childcare providers’ accounts of expulsion finds a patterned process to expulsion: Teachers search for causes and solutions to challenging behaviours. When interventions fail, overwhelmed teachers shift their focus from ‘struggling’ children to ‘bad families’. Once the explanation of behaviour changes from within to outside of the child, expulsion is imminent. Interventions in teachers’ understandings, not only in children's behaviours, are discussed as a possible way to reduce expulsion.     

On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions of pseudo-value regression equations.     

A likelihood based estimator for vector autoregressive processes

A onestep estimator, which is an approximation to the unconditional maximum likelihood estimator (MLE) of the coefficient matrices of a Gaussian vector autoregressive process is presented. The onestep estimator is easy to compute and can be computed using standard software. Unlike the computation of the unconditional MLE, the computation of the onestep estimator does not require any iterative optimization and the computation is numerically stable. In finite samples the onestep estimator generally has smaller mean square error than the ordinary least squares estimator. In a simple model, where the unconditional MLE can be computed, numerical investigation shows that the onestep estimator is slightly worse than the unconditional MLE in terms of mean square error but superior to the ordinary least squares estimator. The limiting distribution of the onestep estimator for processes with some unit roots is derived.     

Nonparametric density deconvolution by weighted kernel estimators

Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate deconvolution density estimation in a very straightforward manner.     

Net migration and migration effectiveness: A comparison between Australia and the United Kingdom, 1976–96

This paper explores the impact of net migration in Australia and the United Kingdom using measures of migration effectiveness computed from period-age migration data for four consecutive five-year periods. Results reported in Part 1 of this paper (Stillwellet al. 2000) suggest that while the overall effectiveness of net migration at the scale of city regions has declined over the twenty-year period in both countries, important geographical variations are evident. Part 2 considers how patterns of migration effectiveness vary by age and presents a single classification of all the city regions on the basis of age-specific effectiveness. More detailed analysis includes spatial patterns for particular age groups and net migration profiles of selected regions. Cohort effects are shown to be important for explaining changes between time periods in these regions.