Estimating utility functions using generalized maximum entropy

This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods.     

A Bayesian Evidence Synthesis for Estimating Campylobacteriosis Prevalence

Stakeholders making decisions in public health and world trade need improved estimations of the burden‐of‐illness of foodborne infectious diseases. In this article, we propose a Bayesian meta‐analysis or more precisely a Bayesian evidence synthesis to assess the burden‐of‐illness of campylobacteriosis in France. Using this case study, we investigate campylobacteriosis prevalence, as well as the probabilities of different events that guide the disease pathway, by (i) employing a Bayesian approach on French and foreign human studies (from active surveillance systems, laboratory surveys, physician surveys, epidemiological surveys, and so on) through the chain of events that occur during an episode of illness and (ii) including expert knowledge about this chain of events. We split the target population using an exhaustive and exclusive partition based on health status and the level of disease investigation. We assume an approximate multinomial model over this population partition. Thereby, each observed data set related to the partition brings information on the parameters of the multinomial model, improving burden‐of‐illness parameter estimates that can be deduced from the parameters of the basic multinomial model. This multinomial model serves as a core model to perform a Bayesian evidence synthesis. Expert knowledge is introduced by way of pseudo‐data. The result is a global estimation of the burden‐of‐illness parameters with their accompanying uncertainty.     

Social ties, task-related communication and first job performance of young engineers

This study investigated the relationships among social ties, task-related communication, and first industrial job performance of a group of young engineers by utilizing a cooperative (coop) engineering program as a natural study. The coop arrangement allowed the researcher to examine the work behavior of these young engineers with similar academic training who were working on comparable work assignments in the same set of organizations.The study found that the job performance of the coop engineers was related to the pattern of work- related communication but not the total number of contacts. Specifically, high performance new engineers were integrated more broadly into the interpersonal communication networks than low performance new engineers. Moreover, high performance new engineers exhibited communication patterns similar to those of high performance veteran engineers working on similar types of technical work. Social ties with the company staff was found to be related positively with job performance, but social ties with other coop engineers was found to be related negatively with job performance, a result which challenges an implicit assumption often made by researchers and managers about the organizational socialization of newcomers. Overall, this study provides an integrative framework for relating social ties and communication as key factors for understanding the job performance of young engineers and presents a methodology for assessing their organizational assimilation.     

Health Risk Assessment of a Modern Municipal Waste Incinerator

During the modernization of the municipal waste incinerator (MWI, maximum capacity of 180,000 tons per year) of Metropolitan Grenoble (405,000 inhabitants), in France, a risk assessment was conducted, based on four tracer pollutants: two volatile organic compounds (benzene and 1, 1, 1 trichloroethane) and two heavy metals (nickel and cadmium, measured in particles). A Gaussian plume dispersion model, applied to maximum emissions measured at the MWI stacks, was used to estimate the distribution of these pollutants in the atmosphere throughout the metropolitan area. A random sample telephone survey (570 subjects) gathered data on time-activity patterns, according to demographic characteristics of the population. Life-long exposure was assessed as a time-weighted average of ambient air concentrations. Inhalation alone was considered because, in the Grenoble urban setting, other routes of exposure are not likely. A Monte Carlo simulation was used to describe probability distributions of exposures and risks. The median of the life-long personal exposures distribution to MWI benzene was 3.2·10^–5 g/m³ (20th and 80th percentiles = 1.5·10^–5 and 6.5·10^–5 g/m³), yielding a 2.6·10^–10 carcinogenic risk (1.2·10^–10–5.4·10^–10). For nickel, the corresponding life-time exposure and cancer risk were 1.8·10^–4 g/m³ (0.9.10^–4 – 3.6·10^–4 g/m³) and 8.6·10^–8 (4.3·10^–8–17.3·10^–8); for cadmium they were respectively 8.3·10^–6 g/m³ (4.0·10^–6–17.6·10^–6) and 1.5·10^–8 (7.2·10^–9–3.1·10^–8). Inhalation exposure to cadmium emitted by the MWI represented less than 1% of the WHO Air Quality Guideline (5 ng/m³), while there was a margin of exposure of more than 10⁹ between the NOAEL (150 ppm) and exposure estimates to trichloroethane. Neither dioxins nor mercury, a volatile metal, were measured. This could lessen the attributable life-long risks estimated. The minute (VOCs and cadmium) to moderate (nickel) exposure and risk estimates are in accord with other studies on modern MWIs meeting recent emission regulations, however.     

Presto: a software package for the simulation of diffusion processes

Conclusion Presto is a software which automatically generates FORTRAN code corresponding to approximation procedures of the solutions of stochastic differential systems.At the present time, it answers only to a few needs, but a diversification of its users and its simple internal structure could easily permit it to be developed towards a more ambitious system.Finally, let us mention that Presto is an INRIA product, which is free for academic institutions, universities, etc, which already have MAPLE and X-Windows licences. Presto can be run on any UNIX station under X-Windows environment.     

Health Risk Assessment After Decontamination of the Beaches Polluted by the Wrecked ERIKA Tanker

Following the wreck of the oil tanker ERIKA off the north-west coast of France in December 1999, cleaning up of the beaches involved considerable work, which in any case could not be perfect. This raised the question of the short- and long-term health risks for the future bathers related to the toxicity of the remaining oil polycyclic aromatic hydrocarbons (PAHs). This risk assessment study was conducted to help health authorities plan risk management policies and inform the public. Thirty-six beaches were selected, representing a sample of the most frequently encountered topographic and beach usage situations; seven "control" beaches, unspoiled by ERIKA, were also investigated. Samples of water and sand were taken from each site, as well as from the surface of rocks. The 16 PAHs selected by the U.S. EPA were quantified in each environment. Several scenarios of exposure were contemplated: (1) a child between 2 and 4 years accidentally ingesting a small ball of fuel, (2) a child daily exposed throughout his holiday-time stay, (3) an adult (including a pregnant woman) spending his/her holidays on the coast, (4) an adult working on the beach, (5) and an adult practicing water sports. Among the available and significant toxicological values, the most conservative ones were selected for computing risks. The sand and water, after decontamination, were slightly polluted (respectively, 7.8 microg/kg and 23.3 ng/l of total 16 PAHs), with values similar to those found in the control beaches. By contrast, the rocky areas in some places were still highly polluted (up to 23 mg/kg on the surface layer). No lethal risk was found for a young child who had accidentally ingested a small ball of fuel. The life-long excess risks for skin cancer and for all other cancers were about 10-5 in scenarios including contact with the polluted rocks. In all other cases, excess risks were considerably lower. The hazard quotient for teratogenic effects was very small, except in scenarios where pregnant women would walk among rocks containing high pollution levels. In conclusion, exposure was mainly associated with polluted water among children, and with spoiled rocks for adults. Despite uncertainties, mainly dealing with the prediction of long-term risks following a short-term exposure, this study showed that beaches where pollution was no longer visible after decontamination did not entail any significant health risks and could be opened to the public.     

THE CHOICE BETWEEN SETS OF REGRESSORS

This paper examines the choice of critical values for testing both non-sequential and nested sequential sets of constraints in the standard linear regression model. Modest increases in (e.g.) t-ratio critical values relative to their one-off values are often sufficient to maintain proper size. A Bayesian decision-theoretic approach, highlighted by the Schwarz (1978) criterion, provides a framework for deriving consistency and asymptotic local power properties of both forms of testing (data mining) algorithms.     

Quantitative Risk Assessment of Listeria monocytogenes in French Cold-Smoked Salmon: I. Quantitative Exposure Assessment

A quantitative assessment of the exposure to Listeria monocytogenes from cold-smoked salmon (CSS) consumption in France is developed. The general framework is a second-order (or two-dimensional) Monte Carlo simulation, which characterizes the uncertainty and variability of the exposure estimate. The model takes into account the competitive bacterial growth between L. monocytogenes and the background competitive flora from the end of the production line to the consumer phase. An original algorithm is proposed to integrate this growth in conditions of varying temperature. As part of a more general project led by the French Food Safety Agency (Afssa), specific data were acquired and modeled for this quantitative exposure assessment model, particularly time-temperature profiles, prevalence data, and contamination-level data. The sensitivity analysis points out the main influence of the mean temperature in household refrigerators and the prevalence of contaminated CSS on the exposure level. The outputs of this model can be used as inputs for further risk assessment.