Inhomogeneous higher-order summary statistics for point processes on linear networks

Statistics and Computing - As a workaround for the lack of transitive transformations on linear network structures, which are required to consider different notions of distributional invariance,...     

Critical success factors for cross-functional teamwork in new product development

There is consensus that the effective implementation of cross-functional teams is critical to new product success. However, such teams face particular challenges because of well-documented barriers between functions. Furthermore, there is little evidence-based guidance for practitioners on how to achieve effective cross-functional teamwork. In order to address this gap, the literature on cross-functional teamwork was analysed to identify critical success factors. Using a heuristic team effectiveness model, these were categorized into six groups: task design, group composition, organizational context, internal processes, external processes and group psychosocial traits. Recent theory on group effectiveness has increasingly recognized the significance of a supportive organizational context, and this is particularly pertinent for cross-functional teams. Key success factors include strategic alignment between functions, a climate supportive of teamwork and team-based accountability. The findings are integrated into a diagnostic model which is intended to be of practical benefit to people designing, leading and facilitating cross-functional new product development teams.     

Non-Proliferation and Disarmament of Nuclear,Chemical and Biological Weapons: New Tasks for the United Nations Specialised Agencies

The reduction or the complete elimination of specific types of weapons, particularly the elimination of all weapons of mass destruction, is one of the most important challenges that the international community should face in the 21th century. In this paper, a group of proposals on the possible roles to be played by the International Atomic Energy Agency (IAEA), the Organisation for the Prohibition of Chemical Weapons (OPCW), the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) and the Conference on Disarmament (CD) is included.     

A penalized likelihood method for nonseparable space–time generalized additive models

In this paper, we study space–time generalized additive models. We apply the penalyzed likelihood method to fit generalized additive models (GAMs) for nonseparable spatio-temporal correlated data in order to improve the estimation of the response and smooth terms of GAMs. The results show that our space–time generalized additive models estimated response and smooth terms reasonable well, and in addition, the mean squared error, mean absolute deviation and coverage intervals improved considerably compared to the classic GAM. An application on particulate matter concentration in the North-Italian region of Piemonte is also presented.     

Quality of life among lifetime victimized men

Quality of life was compared for lifetime victimized (n = 353) and nonvictimized men (n = 167) for demographic and quality of life variables by a cross-sectional design. The univariate analyses showed that victims compared to nonvictims had a lower quality of life, were younger, more often had upper secondary school education, and were more often blue-collar/low white-collar workers, on student allowances, on unemployment, financially strained, and smokers. The regressions revealed that unemployment, financial strain, smoking, depression, and home/public abuse were associated with reduced quality of life among victimized men. Being a blue-collar/low/intermediate white-collar worker and social support were related to increased quality of life. This study may have provided new insights into the experiences of quality of life of victimized men.     

Accurate estimation for extra-Poisson variability assuming random effect models

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.