Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.     

Suicide and mental health problems among Swedish youth in the wake of the 1990s recession

The purpose of this explorative study is to bring together a variety of Swedish data sources with indicators of youth mental health and living conditions in order to illuminate trends during the last decades, elucidate possible determinants of mental ill health and develop hypotheses to explain the observed trend patterns. The analyses in the study reveal some surprising inconsistencies with respect to the mental health trends among young people during the 1990s. Most striking is the pattern of increasing youth unemployment coinciding with almost inverse trends in the rates of suicide. The secular trends in fatal suicides during this period do not show any increase but some actual decrease in sub‐populations despite high sustained levels of unemployment. In contrast, survey data indicate that the general mental health of youth during this period appears to have deteriorated.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

Bootstrap tests: how many bootstraps?

In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the outcome of the test will depend on the sequence of random numbers used to generate the bootstrap samples, and it necessarily results in some loss of power. We examine the extent of this power loss and propose a simple pretest procedure for choosing the number of bootstrap samples so as to minimize experimental randomness. Simulation experiments suggest that this procedure will work very well in practice.     

The Quality of Urban Environments in the Brazilian Amazon

In the Brazilian Amazon during the 1980s,urban population growth outstripped rural growth, andby 1991, most of the region's population resided inurban areas. Much of this urban growth involvedestablishment of unplanned housing with inadequateinfrastructure, which resulted in rising pollution. This paper compares indicators of environmentalquality in urban populations of the Amazon in 1980 and1991, and among different kinds of urban populationsin 1991. The results show that environmental qualityin the region deteriorated during the 1980s as theproduction of and exposure to environmental hazardsrose while resources to ward off hazards eroded. Thefindings also show that environmental quality wasparticularly poor in more rapidly growing urbancenters. The urban Amazon may not afford an adequatestandard of living and this may generate out-migrationfrom the region.     

Social Sources of Failure in Education: The Case in East and Southeast Turkey

The efficiency of education in East and Southeast Turkey (E&SE) has long been a subject of debate. Both regions are similar to each other and different from the other regions in terms of economy, culture, ethnic origin, and languages spoken. Sahin (1997) studied the efficiency of education in the E&SE and proposed that illiteracy is significantly the highest in both regions, the female student population is significantly lower, and students are significantly less successful in nationwide general proficiency examinations (OSS and OYS) compared to all the other regions. Social sources of failure were studied in terms of the distinguishing characteristics of both regions, from both practical and theoretical standpoints. The findings indicated inequality of educational opportunity rooted in geographical, economical, social (cultural, linguistic), and political factors.     

Technocratic Social Work at Home and Abroad: Australia and Iran

Technocratic social work conceived as the neutral application of professional skills has an inbuilt capacity for failing to recognize its social and political effects. Two recent books on social work exemplify this in countries as apparently diverse as Australia and Iran.