The problem of Iraqi nationalism

This article analyses the development of a tenuous Iraqi national identity since the creation of the Iraqi state in 1920. Informed by the ideas of Anthony D. Smith, Benedict Anderson and Eric Hobsbawm, it argues that various political actors in Iraq have sought to reshape historical memory and thus forge a national identity. Despite many setbacks and a long series of authoritarian regimes seeking to appropriate Iraqiness for their own political purposes, and recently the threat from Kurdish irredentism, this article nevertheless contends that an Iraqi cultural ‘ethnicity’ has been created over the past nine decades.     

From knowledge to decision – a case study of sales and operations planning in health care

One of the most important challenges in health care management is balancing resources with fluctuating and uncertain demand. This study examines whether introducing a standardised process for sales and operations planning at a large university hospital improved specialty department managers’ knowledge of planning conditions and if so, whether decisions were made based on this knowledge. Using responses from a survey of 30 specialty department managers at Sahlgrenska University Hospital in Sweden, the analyses indicate that operational knowledge strengthens insights about the needs for strategic decisions, and vice versa. Moreover, knowledge is a necessary, but not sufficient, condition for making decisions at the department level. An overall planning and decision structure is required and should be implemented first at the hospital level to effectively allocate resources.     

A Method to Generate Multivariate Data with the Desired Moments

We show how it is possible to generate multivariate data which has moments arbitrary close to the desired ones. They are generated as linear combinations of variables with known theoretical moments. It is shown how to derive the weights of the linear combinations in both the univariate and the multivariate setting. The use in bootstrapping is discussed and the method is exemplified with a Monte Carlo simulation where the importance of the ability of generating data with control of higher moments is shown.     

A Regression Model for the AUC of Clustered Ordinal Test Results and Working Independent Optimal Weights

We study a regression model on the area under the receiver operating characteristic curves (AUC) for clustered (or repeatedly measured) test results. To account for cluster information, we consider a weighted estimating equation for Dodd and Pepe (2003 Dodd , L. , Pepe , M. ( 2003 ). Semiparametric regression for the area under the receiver operating charateristic curve . Journal of the American Statistical Association 98 : 409 – 417 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar])'s regression model with working independence weights. We find the optimal weight in the given class of working independence weights to minimize the variance (or MSE) of regression estimators. We apply the proposed procedure to analyzing our recent experiment on diagnosing a liver disorder. In this experiment, we investigated MRI images of patients having symptoms of potential liver disorder to compare the performance of different MRI picturing methods in testing for liver disorders.     

Intensive family preservation services in Flanders: an outcome study

Set up as a one-group pre-test–post-test follow-up design, this study examines the outcomes of intensive family preservation services (IFPS) in Flanders addressing the following research questions: Does IFPS make a significant difference concerning (1) the interaction between parent and child? (2) social support as experienced by the parent? (3) stress and empowerment of the parent? and (4) the strengths and difficulties of the minor? Families seem to indicate that their family situation is more manageable, although still, professional help is needed. Parents feel more resilient about the situation and indicate that they can get along with their child better than before the crisis intervention. Parents still feel uncertain about the future and about their role as a parent. There seems to be no indications that their level and quality of social contacts have changed. Further research is suggested to provide more in-depth information about the conditions associated with processes of change during an IFPS intervention.     

Regularized Gaussian belief propagation

Belief propagation (BP) has been applied in a variety of inference problems as an approximation tool. BP does not necessarily converge in loopy graphs, and even if it does, is not guaranteed to provide exact inference. Even so, BP is useful in many applications due to its computational tractability. In this article, we investigate a regularized BP scheme by focusing on loopy Markov graphs (MGs) induced by a multivariate Gaussian distribution in canonical form. There is a rich literature surrounding BP on Gaussian MGs (labelled Gaussian belief propagation or GaBP), and this is known to experience the same problems as general BP on graphs. GaBP is known to provide the correct marginal means if it converges (this is not guaranteed), but it does not provide the exact marginal precisions. We show that our adjusted BP will always converge, with sufficient tuning, while maintaining the exact marginal means. As a further contribution we show, in an empirical study, that our GaBP variant can accelerate GaBP and compares well with other GaBP-type competitors in terms of convergence speed and accuracy of approximate marginal precisions. These improvements suggest that the principle of regularized BP should be investigated in other inference problems. The selection of the degree of regularization is addressed through the use of two heuristics. A by-product of GaBP is that it can be used to solve linear systems of equations; the same is true for our variant and we make an empirical comparison with the conjugate gradient method.     

Can Reporting Heterogeneity Explain Differences in Depressive Symptoms Across Europe?

Depression is one of the leading causes of disability in the developed world. Previous studies have shown varying depression prevalence rates between European countries, and also within countries, between socioeconomic groups. However, it is unclear whether these differences reflect true variations in prevalence or whether they are attributable to systematic differences in reporting styles (reporting heterogeneity) between countries and socioeconomic groups. In this study, we examine the prevalence of three depressive symptoms (mood, sleeping and concentration problems) and their association with educational level in 10 European countries, and examine whether these differences can be explained by differences in reporting styles. We use data from the first and second waves of the COMPARE study, comprising a sub-sample of 9,409 adults aged 50 and over in 10 European countries covered by the Survey of Health, Ageing and Retirement in Europe. We first use ordered probit models to estimate differences in the prevalence of self-reported depressive symptoms by country and education. We then use hierarchical ordered probit models to assess differences controlling for reporting heterogeneity. We find that depressive symptoms are most prevalent in Mediterranean and Eastern European countries, whereas Sweden and Denmark have the lowest prevalence. Lower educational level is associated with higher prevalence of depressive symptoms in all European regions, but this association is weaker in Northern European countries, and strong in Eastern European countries. Reporting heterogeneity does not explain these cross-national differences. Likewise, differences in depressive symptoms by educational level remain and in some regions increase after controlling for reporting heterogeneity. Our findings suggest that variations in depressive symptoms in Europe are not attributable to differences in reporting styles, but are instead likely to result from variations in the causes of depressive symptoms between countries and educational groups.     

Bayesian variable selection with strong heredity constraints

In this paper, we propose a Bayesian variable selection method for linear regression models with high-order interactions. Our method automatically enforces the heredity constraint, that is, a higher order interaction term can exist in the model only if both of its parent terms are in the model. Based on the stochastic search variable selection George and McCulloch (1993), we propose a novel hierarchical prior that fully considers the heredity constraint and controls the degree of sparsity simultaneously. We develop a Markov chain Monte Carlo (MCMC) algorithm to explore the model space efficiently while accounting for the heredity constraint by modifying the shotgun stochastic search algorithm Hans et al. (2007). The performance of the new model is demonstrated through comparisons with other methods. Numerical studies on both real data analysis and simulations show that our new method tends to find relevant variable more effectively when higher order interaction terms are considered.     

Second-order refined peaks-over-threshold modelling for heavy-tailed distributions

Modelling excesses over a high threshold using the Pareto or generalized Pareto distribution (PD/GPD) is the most popular approach in extreme value statistics. This method typically requires high thresholds in order for the (G)PD to fit well and in such a case applies only to a small upper fraction of the data. The extension of the (G)PD proposed in this paper is able to describe the excess distribution for lower thresholds in case of heavy-tailed distributions. This yields a statistical model that can be fitted to a larger portion of the data. Moreover, estimates of tail parameters display stability for a larger range of thresholds. Our findings are supported by asymptotic results, simulations and a case study.