Intake fraction for multimedia pollutants: a tool for life cycle analysis and comparative risk assessment.

We employ the intake fraction (iF) as an effective tool for expressing the source-to-intake relationship for pollutant emissions in life cycle analysis (LCA) or comparative risk assessment. Intake fraction is the fraction of chemical mass emitted into the environment that eventually passes into a member of the population through inhalation, ingestion, or dermal exposure. To date, this concept has been primarily applied to pollutants whose primary route of exposure is inhalation. Here we extend the use of iF to multimedia pollutants with multiple exposure pathways. We use a level III multimedia model to calculate iF for TCDD and compare the result to one calculated from measured levels of dioxin toxic equivalents in the environment. We calculate iF for emissions to air and surface water for 308 chemicals. We correlate the primary exposure route with the magnitudes of the octanol-water partition coefficient, Kow, and of the air-water partitioning coefficient (dimensionless Henry constant), Kaw. This results in value ranges of Kow and Kaw where the chemical exposure route can be classified with limited input data requirements as primarily inhalation, primarily ingestion, or multipathway. For the inhalation and ingestion dominant pollutants, we also define empirical relationships based on chemical properties for quantifying the intake fraction. The empirical relationships facilitate rapid evaluation of many chemicals in terms of the intake. By defining a theoretical upper limit for iF in a multimedia environment we find that iF calculations provide insight into the multimedia model algorithms and help identify unusual patterns of exposure and questionable exposure model results.     

Dynamique de l’éducation bilingue interculturelle dans l'Amazonie brésilienne

In Brazil – and more largely in Latin America – the fight of the indigenous movements for the demarcation of their territory and the installation of an intercultural school education contributed to the constitutional changes of the years 1980–1990 which led these States to regard themselves from then on as pluricultural and multiethnic nations and to recognize collective rights specific to native people and tribes living on their territory. The author analyzes the advent and the development of this intercultural bilingual education in two border regions of the State of Amazonas (Alto Solimões and Alto Rio Negro) near the populations Ticuna, Baniwa and Tukano during the years 1990 and 2000. He shows in particular how the indigenous school, an assimilationist instrument for the Occidental and Christian culture until the 1980s, has been transformed by supporting the reappropriation of the traditional knowledge; meanwhile this school has opened itself to ‘Western’ knowledge in order to make it possible for the younger generation to acquire the ability to go towards evolution.     

Analysing the effects of tax-benefit reforms on income distribution: a decomposition approach

To assess the impact of tax-benefit policy changes on income distribution over time, we suggest a methodology based on counterfactual tax-benefit simulations. Changes in inequality/poverty indices are decomposed into three contributions: changes in the tax-benefit structure, changes in nominal levels of market incomes and tax-benefit money parameters, and all other changes, including shifts in market income inequality and demographic composition. The policy effect can be evaluated conditionally on base-period data or end-period data; it is also possible to average the two measures, which corresponds to an application of the Shapley value method as reinterpreted by Shorrocks (Decomposition Procedures for Distributional Analysis: A Unified Framework Based on the Shapley Value, University of Essex and Institute for Fiscal Studies, Wivenhoe Park, 1999). The decomposition is used to quantify the relative role of policy changes on inequality/poverty trends in France and Ireland in the 1990s. When end-period data are not available, e.g., for forward looking analysis of possible reforms, the base weighted decomposition helps to extract an absolute measure of the impact of tax-benefit changes on income distribution as evaluated against a distributionally neutral benchmark; in our application, it is not significantly different from the policy effect stemming from the Shorrocks-Shapley decomposition. Estimates of this type are derived to assess recent policy changes in twelve European countries.     

Maybe Next Month? Temperature Shocks and Dynamic Adjustments in Birth Rates

We estimate the effects of temperature shocks on birth rates in the United States between 1931 and 2010. We find that days with a mean temperature above 80°F cause a large decline in birth rates 8 to 10 months later. Unlike prior studies, we demonstrate that the initial decline is followed by a partial rebound in births over the next few months, implying that populations mitigate some of the fertility cost by shifting conception month. This shift helps explain the observed peak in late-summer births in the United States. We also present new evidence that hot weather most likely harms fertility via reproductive health as opposed to sexual activity. Historical evidence suggests that air conditioning could be used to substantially offset the fertility costs of high temperatures.     

Exponential inequalities for unbounded functions of geometrically ergodic Markov chains: applications to quantitative error bounds for regenerative Metropolis algorithms

ABSTRACT

The aim of this note is to investigate the concentration properties of unbounded functions of geometrically ergodic Markov chains. We derive concentration properties of centred functions with respect to the square of Lyapunov's function in the drift condition satisfied by the Markov chain. We apply the new exponential inequalities to derive confidence intervals for Markov Chain Monte Carlo algorithms. Quantitative error bounds are provided for the regenerative Metropolis algorithm of [Brockwell and Kadane Identification of regeneration times in MCMC simulation, with application to adaptive schemes. J Comput Graphical Stat. 2005;14(2)].     

Assessing the fit of finite mixture distributions

Mixture distributions have become a very flexible and common class of distributions, used in many different applications, but hardly any literature can be found on tests for assessing their goodness of fit. We propose two types of smooth tests of goodness of fit for mixture distributions. The first test is a genuine smooth test, and the second test makes explicit use of the mixture structure. In a simulation study the tests are compared to some traditional goodness of fit tests that, however, are not customised for mixture distributions. The first smooth test has overall good power and generally outperforms the other tests. The second smooth test is particularly suitable for assessing the fit of each component distribution separately. The tests are applicable to both continuous and discrete distributions and they are illustrated on three medical data sets.     

Cyberbullying and health: A preliminary investigation of the experiences of Canadian gay and bisexual adult men

The majority of cyberbullying studies are within the school environment. This quantitative study investigates cyberbullying among Canadian gay and bisexual men outside of the educational setting through a cross-sectional survey. Of the 7,430 respondents, 4.6% reported cyberbullying in the past year, with younger, Aboriginal, lower-educated, lower-income respondents reporting higher odds of experiencing cyberbullying. Victimization from cyberbullying was significantly associated with experiences of antigay discrimination as well as worry about antigay prejudice. A variety of negative health outcomes were also associated with cyberbullying, including intimate partner violence and suicidality. Implications for social service providers and future research are discussed.     

On-line expectation–maximization algorithm for latent data models

Summary.  We propose a generic on-line (also sometimes called adaptive or recursive) version of the expectation–maximization (EM) algorithm applicable to latent variable models of independent observations. Compared with the algorithm of Titterington, this approach is more directly connected to the usual EM algorithm and does not rely on integration with respect to the complete-data distribution. The resulting algorithm is usually simpler and is shown to achieve convergence to the stationary points of the Kullback–Leibler divergence between the marginal distribution of the observation and the model distribution at the optimal rate, i.e. that of the maximum likelihood estimator. In addition, the approach proposed is also suitable for conditional (or regression) models, as illustrated in the case of the mixture of linear regressions model.     

Adaptive importance sampling in general mixture classes

In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the performance of importance sampling, as measured by an entropy criterion. The method, called M-PMC, is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performance of the proposed scheme is studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme. This work has been supported by the Agence Nationale de la Recherche (ANR) through the 2006–2008 project ’ . Both last authors are grateful to the participants to the BIRS meeting on “Bioinformatics, Genetics and Stochastic Computation: Bridging the Gap”, Banff, for their comments on an earlier version of this paper. The last author also acknowledges an helpful discussion with Geoff McLachlan. The authors wish to thank both referees for their encouraging comments.     

Relative effect sizes for measures of risk

Effect sizes are an important component of experimental design, data analysis, and interpretation of statistical results. In some situations, an effect size of clinical or practical importance may be unknown to the researcher. In other situations, the researcher may be interested in comparing observed effect sizes to known standards to quantify clinical importance. In these cases, the notion of relative effect sizes (small, medium, large) can be useful as benchmarks. Although there is generally an extensive literature on relative effect sizes for continuous data, little of this research has focused on relative effect sizes for measures of risk that are common in epidemiological or biomedical studies. The aim of this paper, therefore, is to extend existing relative effect sizes to the relative risk, odds ratio, hazard ratio, rate ratio, and Mantel–Haenszel odds ratio for related samples. In most scenarios with equal group allocation, effect sizes of 1.22, 1.86, and 3.00 can be taken as small, medium, and large, respectively. The odds ratio for a non rare event is a notable exception and modified relative effect sizes are 1.32, 2.38, and 4.70 in that situation.