Validation of Multimedia Models Assessing Exposure to PAHs—The SOLEX Study

Polluted soils have become a public health problem. While population exposure to soil pollutants is generally quantified using multimedia models, their estimations have not been validated, and studies that attempted to do so are scarce. The objective of the SOLEX study was to compare the predictions of pyrene exposure levels (converted into 1 hydroxypyrene) computed by several models with the results of urinary 1-hydropyrene (1-HOP) assays among 110 employees working at three sites polluted during their past use as manufactured gas plants. Four models were used: AERIS (Canada), CalTOX (California, USA), CLEA (UK), and HESP (The Netherlands). Three occupational exposure scenarios--with office, mixed, and outdoor workers--were constructed, based upon job activities during two measurement campaigns, one in winter and one in summer. The exposure levels estimated by the four models could differ markedly (from 7 up to 80 times) according to the exposure scenario. Also, the predominant exposure routes differed according to the model (direct soil ingestion for HESP and CalTOX, inhalation for AERIS, and dermal absorption for CLEA). The predictions of CalTOX are consistent with the 1-HOP measurements for all the scenarios. For HESP, the consistency is observed for the scenarios, office and mixed, for which the pyrene level in the soil is low. AERIS and CLEA yield results that are systematically above the 1-HOP measurements. This study confirms that validation of the models is crucial and points out to the need to proceed to assess components of the models that are the most influential using appropriate statistical analysis in combination with true field data.     

SEPARATION, WEAK EXOGENEITY, AND P-T DECOMPOSITION IN COINTEGRATED VAR SYSTEMS WITH COMMON FEATURES

The aim of this paper is to study the concept of separability in multiple nonstationary time series displaying both common stochastic trends and common stochastic cycles. When modeling the dynamics of multiple time series for a panel of several entities such as countries, sectors, firms, imposing some form of separability and commonalities is often required to restrict the dimension of the parameter space. For this purpose we introduce the concept of common feature separation and investigate the relationships between separation in cointegration and separation in serial correlation common features. Loosely speaking we investigate whether a set of time series can be partitioned into subsets such that there are serial correlation common features within the sub-groups only. The paper investigates three issues. First, it provides conditions for separating joint cointegrating vectors into marginal cointegrating vectors as well as separating joint short-term dynamics into marginal short-term dynamics. Second, conditions for making permanent-transitory decompositions based on marginal systems are given. Third, issues of weak exogeneity are considered. Likelihood ratio type tests for the different hypotheses under study are proposed. An empirical analysis of the link between economic fluctuations in the United States and Canada shows the practical relevance of the approach proposed in this paper.     

Bayesian optimal design for changepoint problems

We investigate Bayesian optimal designs for changepoint problems. We find robust optimal designs which allow for arbitrary distributions before and after the change, arbitrary prior densities on the parameters before and after the change, and any log‐concave prior density on the changepoint. We define a new design measure for Bayesian optimal design problems as a means of finding the optimal design. Our results apply to any design criterion function concave in the design measure. We illustrate our results by finding the optimal design in a problem motivated by a previous clinical trial. The Canadian Journal of Statistics 37: 495–513; 2009 © 2009 Statistical Society of Canada     

A test for crossing receiver operating characteristic (roc) curves

The receiver operating characteristic (ROC) curve gives a graphical representation of sensitivity and specificity of a prediction model when varying the decision treshold on a diagnostic criterion. A classical test for comparing the overall accuracies for two models -1 and 2- is based on the difference between ROC curves areas - related to its standard error. This test is designed for the situation where ROC curve 1 caps ROC curve 2. Often both curves cross :in this paper, a new test, based on the integrated difference between the curves, is proposed to deal with this situation. In a simulation experiment, the new test was less powerful than the old test for detecting an overall superiority, but much more powerfull against the crossing alternative.     

Importance sampling with the generalized exponential power density

In this paper, the generalized exponential power (GEP) density is proposed as an importance function in Monte Carlo simulations in the context of estimation of posterior moments of a location parameter. This density is divided in five classes according to its tail behaviour which may be exponential, polynomial or logarithmic. The notion of p-credence is also defined to characterize and to order the tails of a large class of symmetric densities by comparing their tails to those of the GEP density.The choice of the GEP density as an importance function allows us to obtain reliable and effective results when p-credences of the prior and the likelihood are defined, even if there are conflicting sources of information. Characterization of the posterior tails using p-credence can be done. Hence, it is possible to choose parameters of the GEP density in order to have an importance function with slightly heavier tails than the posterior. Simulation of observations from the GEP density is also addressed.