Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose‐response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the “hybrid” method proposed by Crump, two strategies of BMA, including both “maximum likelihood estimation based” and “Markov Chain Monte Carlo based” methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose‐response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose‐response data.     

State of the Art in Risk Analysis of Workforce Criticality Influencing Disaster Preparedness for Interdependent Systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.     

Emotional Climate in Families Experiencing Homelessness: Associations with Child Affect and Socioemotional Adjustment in School

This study examined associations among family‐level risks, emotional climate, and child adjustment in families experiencing homelessness. Emotional climate, an indirect aspect of emotion socialization, was indexed by parents’ expressed emotion while describing their children. Sociodemographic risk and parent internalizing distress were hypothesized to predict more negativity and less warmth in the emotional climate. Emotional climate was expected to predict observer‐rated child affect and teacher‐reported socioemotional adjustment, mediating effects of risk. Participants were 138 homeless parents (64 percent African‐American) and their four‐ to six‐year‐old children (43.5 percent male). During semi‐structured interviews, parents reported demographic risks and internalizing distress and completed a Five Minute Speech Sample about their child, later rated for warmth and negativity. Children's positive and negative affect were coded from videotapes of structured parent‐child interaction tasks. Socioemotional adjustment (externalizing behavior, peer acceptance, and prosocial behavior) was reported by teachers a few months later. Hypotheses were partially supported. Parent internalizing distress was associated with higher parent negativity, which was linked to more negative affect in children, and parent warmth was associated with children's positive affect. Neither emotional climate nor child affect predicted teacher‐reported externalizing behavior or peer acceptance, but parental negativity and male sex predicted lower prosocial behavior in the classroom. Future research directions and clinical implications are discussed.     

On residual lifetimes in sequential (n ? k?+?1)-out-of-n systems

Sequential order statistics is an extension of ordinary order statistics. They model the successive failure times in sequential k-out-of-n systems, where the failures of components possibly affect the residual lifetimes of the remaining ones. In this paper, we consider the residual lifetime of the components after the kth failure in the sequential (n − k + 1)-out-of-n system. We extend some results on the joint distribution of the residual lifetimes of the remaining components in an ordinary (n − k + 1)-out-of-n system presented in Bairamov and Arnold (Stat Probab Lett 78(8):945–952, 2008) to the case of the sequential (n − k + 1)-out-of-n system.     

Improved bridge constructs for stochastic differential equations

We consider the task of generating discrete-time realisations of a nonlinear multivariate diffusion process satisfying an Itô stochastic differential equation conditional on an observation taken at a fixed future time-point. Such realisations are typically termed diffusion bridges. Since, in general, no closed form expression exists for the transition densities of the process of interest, a widely adopted solution works with the Euler–Maruyama approximation, by replacing the intractable transition densities with Gaussian approximations. However, the density of the conditioned discrete-time process remains intractable, necessitating the use of computationally intensive methods such as Markov chain Monte Carlo. Designing an efficient proposal mechanism which can be applied to a noisy and partially observed system that exhibits nonlinear dynamics is a challenging problem, and is the focus of this paper. By partitioning the process into two parts, one that accounts for nonlinear dynamics in a deterministic way, and another as a residual stochastic process, we develop a class of novel constructs that bridge the residual process via a linear approximation. In addition, we adapt a recently proposed construct to a partial and noisy observation regime. We compare the performance of each new construct with a number of existing approaches, using three applications.     

Transformational Leadership, Organizational Culture, Organizational Effectiveness, and Programme Outcomes in Non-Governmental Organizations

This study examines whether transformational leadership influences organizational culture that furthers NGOs?? effectiveness. It also examines whether transformational leadership at the top directly influences NGOs?? effectiveness. Further, it tests whether NGO effectiveness improves the programme outcomes in terms of health, income, education, and happiness of villagers. Data were collected from 312 NGOs in Jharkhand state (India). Transformational leadership, organizational culture, and NGO effectiveness were assessed from NGO personnel using standard instruments, and programme outcomes on health, income, education, and happiness were evaluated from villagers using wooden cubes. Findings reveal that transformational leadership builds organizational culture that furthers NGO effectiveness. Transformational leadership does not influence directly NGO effectiveness but it enhances NGO effectiveness promoting organizational culture. Furthermore, NGO effectiveness improves the outcomes of programmes undertaken by NGOs in terms of better health, income, education, and happiness of beneficiaries.     

Culture Change in Aged-Care Facilities: A Café's Contribution to Transforming the Physical and Social Environment

Objective: To discuss the potential of a café on the premises of an aged-care facility as a vehicle for culture change. Method: Secondary analysis of primary qualitative data that explored the value of a café in an aged-care facility, compared with culture-change principles established from a literature review. Results: Secondary analysis established congruence between culture-change principles and the themes established in the primary qualitative data. A café, in one aged-care facility, has facilitated the following dimensions of culture change for residents: individualized care, facilitation of meaningful relationships, opportunities for participation in life roles, and creation of a sense of belonging. Conclusion: Culture change can be achieved through environmental innovations, such as a café, where food and dining maximize social opportunities and create a homelike environment that facilitates continuation of residents' identity.     

Selecting the normal population with the smallest variance: A restricted subset selection rule

Consider k( ? 2) normal populations whose means are all known or unknown and whose variances are unknown. Let σ²_[1] ? ??? ? σ_[k]² denote the ordered variances. Our goal is to select a non empty subset of the k populations whose size is at most m(1 ? m ? k ? 1) so that the population associated with the smallest variance (called the best population) is included in the selected subset with a guaranteed minimum probability P* whenever σ²_[2]/σ_[1]² ? δ* > 1, where P* and δ* are specified in advance of the experiment. Based on samples of size n from each of the populations, we propose and investigate a procedure called R_BCP. We also derive some asymptotic results for our procedure. Some comparisons with an earlier available procedure are presented in terms of the average subset sizes for selected slippage configurations based on simulations. The results are illustrated by an example.     

Bayesian averaging of classical estimates in asymmetric vector autoregressive models

The estimated vector autoregressive (VAR) model is sensitive to model misspecifications, resulting to biased and inconsistent parameter estimates. This article extends the Bayesian averaging of classical estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of asymmetric VAR models. The proposed procedure was applied to simulated data from various forms of model misspecifications. The results of the simulation suggest that, under misspecification problems, particularly if an important variable and moving average (MA) terms were omitted, the proposed procedure gives robust results and better forecasts than the automatically selected equal lag-length VAR model.