Goodness-of-Fit Methods for Probabilistic Index Models

A class of semiparametric regression models, called probabilistic index models, has been recently proposed. Because these models are semiparametric, inference is only valid when the proposed model is consistent with the underlying data-generating model. However, no formal goodness-of-fit methods for these probabilistic index models exist yet. We propose a test and a graphical tool for assessing the model adequacy. Simulation results indicate that both methods succeed in detecting lack-of-fit. The methods are also illustrated on a case study.     

Estimation in a Two Variance Components Model When one Component is Known

We study a mixed linear model with two variance components. We suppose that one component is known. The objective of the paper is the estimation of the unknown component. The usual MINQE estimators seem to be unadapted to the problem. So we propose a new family of quadratic estimators, based on a natural class of estimators and the idea upon which the MINQE theory is built. All the estimators are compared on simulated data.     

Discounting health and money: New evidence using a more robust method

This study compares discounting for money and health in a field study. We applied the direct method, which measures discounting independent of utility, in a representative French sample, interviewed at home by professional interviewers. We found more discounting for money than for health. The median discount rates (6.5% for money and 2.2% for health) were close to market interest rates, suggesting that at the aggregate level the direct method solves the puzzle of unrealistically high discount rates typically observed in applied economics. Constant discounting fitted the data better than the hyperbolic discounting models that we considered. The substantial individual heterogeneity in discounting was correlated with age and occupation.     

Bivariate censored regression relying on a new estimator of the joint distribution function

In this paper we study a class of M  -estimators in a regression model under bivariate random censoring and provide a set of sufficient conditions that ensure asymptotic n^1/2-convergence$n^{1/2}\mathrm{-}\mathrm{convergence}$. The cornerstone of our approach is a new estimator of the joint distribution function of the censored lifetimes. A copula approach is used to modelize the dependence structure between the bivariate censoring times. The resulting estimators present the advantage of being easily computable. A simulation study enlighten the finite sample behavior of this technique.     

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.     

Risk Assessment of Listeriosis Linked to the Consumption of Two Soft Cheeses Made from Raw Milk: Camembert of Normandy and Brie of Meaux

This article reports a quantitative risk assessment of human listeriosis linked to the consumption of soft cheeses made from raw milk. Risk assessment was based on data purposefully acquired inclusively over the period 2000-2001 for two French cheeses, namely: Camembert of Normandy and Brie of Meaux. Estimated Listeria monocytogenes concentration in raw milk was on average 0.8 and 0.3 cells/L, respectively, in Normandy and Brie regions. A Monte Carlo simulation was used to account for the time-temperature history of the milk and cheeses from farm to table. It was assumed that cell progeny did not spread within the solid cheese matrix (as they would be free to do in liquid broth). Interaction between pH and temperature was accounted for in the growth model. The simulated proportion of servings with no L. monocytogenes cell was 88% for Brie and 82% for Camembert. The 99th percentile of L. monocytogenes cell numbers in servings of 27 g of cheese was 131 for Brie and 77 for Camembert at the time of consumption, corresponding respectively to three and five cells of L. monocytogenes per gram. The expected number of severe listeriosis cases would be < or =10(-3) and < or =2.5 x 10(-3) per year for 17 million servings of Brie of Meaux and 480 million servings of Camembert of Normandy, respectively.     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

La norme ISO 45001 et les controverses autour de la régulation transnationale privée de la sécurité et de la santé au travail

Les auteurs analysent la genèse de la norme ISO 45001 sur la sécurité et la santé au travail, une initiative de régulation transnationale privée. Ils décrivent les controverses qui ont surgi à cette occasion, en se fondant sur des entretiens avec des personnes associées à l'élaboration, l'approbation et la diffusion initiale du texte, ainsi que sur une analyse qualitative des documents internes au comité chargé de sa finalisation. À l'instar de normes précédentes sur l'environnement – ISO 14001 – et la responsabilité sociale des entreprises – ISO 26000 –, ce nouvel instrument suscite de vives préoccupations, car il traite de questions politiques, sociales et juridiques déterminantes.