Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.     

Risk Assessment for Invasive Species

Although estimates vary, there is a broad agreement that invasive species impose major costs on the U.S. economy, as well as posing risks to nonmarket environmental goods and services and to public health. The domestic effort to manage risks associated with invasive species is coordinated by the National Invasive Species Council (NISC), which is charged with developing a science-based process to evaluate risks associated with the introduction and spread of invasive species. Various international agreements have also elevated invasive species issues onto the international policy agenda. The World Trade Organization (WTO) Sanitary and Phytosanitary (SPS) Agreement establishes rights and obligations to adhere to the discipline of scientific risk assessment to ensure that SPS measures are applied only to the extent required to protect human, animal, and plant health, and do not constitute arbitrary or unjustifiable technical barriers to trade. Currently, however, the field of risk assessment for invasive species is in its infancy. Therefore, there is a pressing need to formulate scientifically sound methods and approaches in this emerging field, while acknowledging that the demand for situation-specific empirical evidence is likely to persistently outstrip supply. To begin addressing this need, the Society for Risk Analysis Ecological Risk Assessment Specialty Group and the Ecological Society of America Theoretical Ecology Section convened a joint workshop to provide independent scientific input into the formulation of methods and processes for risk assessment of invasive species to ensure that the analytic processes used domestically and internationally will be firmly rooted in sound scientific principles.     

Understanding information systems infrastructure in engineering SMEs: A case study

The benefits of integrated enterprise-wide information systems (IS), such as Enterprise Resource Planning (ERP) systems, are widely accepted. The implementation of such systems in large organisations is established with many in their second or third generation. However, in small to medium-sized enterprises (SMEs) the maturity and extent of their IS infrastructure is less clear. In engineering SMEs, this infrastructure needs to support not only business processes but also the activities of design and manufacture.In this case study, our goal is to provide a better understanding of current IS infrastructure, the functional elements of IS infrastructure, integration and expenditures are explored in ten engineering SMEs. A model of IS functionality is developed and used to identify and classify functional elements within the firms and to compare and contrast the IS infrastructures. The findings are contrasted with the SME literature and conclusions and implications developed.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.     

Federal budget helps drive health policy. A close-up look at the budget process and how it affects health care funding

Crafting the budget is an annual process that Congress and the administration use to define the national policy agenda. It is a massive undertaking to decide how more than two trillion dollars is spent by the federal government. Over 20 percent of the budget is used for health care. The budget is a political document that defines the priorities of the federal government and directly influences overall health policy. Its construction deserves to be followed and influenced by physicians to ensure the health of the public.     

Economic Production Lot Sizing with Periodic Costs and Overtime*

Traditional approaches for modeling economic production lot‐sizing problems assume that a single, fixed equipment setup cost is incurred each time a product is run, regardless of the quantity manufactured. This permits multiple days of production from one production setup. In this paper, we extend the model to consider additional fixed charges, such as cleanup or inspection costs, that are associated with each time period's production. This manufacturing cost structure is common in the food, chemical, and pharmaceutical industries, where process equipment must be sanitized between item changeovers and at the end of each day's production. We propose two mathematical problem formulations and optimization algorithms. The models' unique features include regular time production constraints, a fixed charge for each time period's production, and the availability of overtime production capacity. Experimental results indicate the conditions under which our algorithms' performance is superior to traditional approaches. We also test the procedures on a set of lot‐sizing problems facing a national food processor and document their potential economic benefit.