Loss functions for estimation of extrema with an application to disease mapping

It is often of interest to find the maximum or near maxima among a set of vector‐valued parameters in a statistical model; in the case of disease mapping, for example, these correspond to relative‐risk “hotspots” where public‐health intervention may be needed. The general problem is one of estimating nonlinear functions of the ensemble of relative risks, but biased estimates result if posterior means are simply substituted into these nonlinear functions. The authors obtain better estimates of extrema from a new, weighted ranks squared error loss function. The derivation of these Bayes estimators assumes a hidden‐Markov random‐field model for relative risks, and their behaviour is illustrated with real and simulated data.     

Collaborative evaluation and market research converge: an innovative model agricultural development program evaluation in Southern Sudan

In June and July 2006 a team of outside experts arrived in Yei, Southern Sudan through an AID project to provide support to a local agricultural development project. The team brought evaluation, agricultural marketing and financial management expertise to the in-country partners looking at steps to rebuild the economy of the war ravaged region. A partnership of local officials, agricultural development staff, and students worked with the outside team to craft a survey of agricultural traders working between northern Uganda and Southern Sudan the steps approach of a collaborative model. The goal was to create a market directory of use to producers, government officials and others interested in stimulating agricultural trade. The directory of agricultural producers and distributors served as an agricultural development and promotion tool as did the collaborative process itself.     

Extending Bayesian back-calculation to estimate age and time specific HIV incidence

Lifetime Data Analysis - CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their...     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

It Takes Two to Fight in School,Too: A Social Relations Model of the Psychometric Properties and Relative Variance of Dyadic Aggression and Victimization in Middle School

Aggressive behavior has been well studied in terms of interindividual differences among aggressors and victims, but has been understudied, especially within naturalistic contexts, in terms of aggressor–victim relationships. The social relations model (SRM) is a powerful conceptual and analytic tool for studying dyadic phenomena, and we describe the use of multivariate SRM to study aggression. Boys and girls (N = 210) in middle schools (sixth and seventh grades) completed a newly created dyadic aggression and victimization inventory (DAVI). Results support the reliability and validity of the DAVI in assessing inter‐individual and inter‐dyadic differences in aggression and victimization. Occurrences of aggression were accounted for primarily by inter‐dyadic variability, indicating the importance of considering aggressor–victim relationships. We discuss the implications of this relationship focus for future research and intervention efforts.     

Cognitive-behavioral treatment for adolescents who sexually offend and their families: individual and family applications in a collaborative outpatient program

This article describes an outpatient treatment program for adolescent sexual abusers that was established by a mental health agency in collaboration with a specialized probation program in the juvenile court. Individualized treatment is based on a comprehensive clinical assessment with the youth and guardian, for which examples are provided. Given the heterogeneity of this population, we describe several treatment strategies directed to various individual or family clinical targets, including psychiatric disorders, sexual deviance and sexuality, normal adolescent development and adaptive skills, and parent and family relationships. Ongoing collaborative and coordination issues are also reviewed. The integration of mental health and probationary services provides a balanced approach to the community management and treatment of the low-risk, primarily first-time, adolescent sexual offender.     

Forecasting: A hybrid approach

It has recently been shown that the Box-Jenkins approach to forecasting time series is superior to an econometric approach over a relatively short horizon. The results here support this contention. A combination of the two approaches, however, proves to be clearly superior to either one separately. By taking account of changes in economic and weather related variables in a time series model, improved forecasts are obtained.     

The impact of acquisitions on firm performance: A review of the evidence

This paper presents a review of empirical research on the impact of acquisitions on firm performance. The evidence suggests that, in the short run, acquisitions have at best an insignificant impact on shareholder wealth. Long‐run performance analysis reveals overwhelmingly negative returns, while the evidence using accounting performance measures is mixed. The review also examines the impact of bid characteristics on performance. The acquisition of hostile targets, transactions that are paid for with cash and acquisitions of larger targets are associated with superior (or at least less negative) performance, while there is mixed evidence on the benefits of related acquisitions. A number of recent studies find that acquirers with superior pre‐bid performance tend to experience significant underperformance in the post‐bid period.     

Transect-spacing design of ice cores on the Antarctic continent

Chemical analyses of ice cores, drilled deep into an ice sheet, provide a historical record of the earth's atmosphere that dates back as far as 400,000–500,000 years. Although the atmosphere mixes quite well, it is recognized that spatial variability associated with ice-core locations should be allowed for. In this article, spatial statistical methodology is applied to the design question of finding the best spacing of ice-core locations on a partial transect of Antarctica.