Les PME libanaises de l'agroalimentaire : Quel potentiel RSE ?

International audienceCette étude vise à cartographier les perceptions managériales dans les PME agroalimentaires libanaises en matière de RSE selon le modèle de Quazi et O'Brien (2000) repris au Moyen Orient par Jamali et al. (2009). Dans le cadre des deux principaux courants antagonistes en matière de RSE, à savoir, d'un côté, une vision classique et, d'un autre côté, une vision élargie de l'entreprise et de ses responsabilités, nous avons développé le modèle bidimensionnel de Quazi et O'Brien (2000) en le transposant au contexte du Liban sur le secteur des PME agroalimentaires à travers une enquête par questionnaire auprès de 155 managers.</p

Early Detection of Emergent Events Based on an Extremal Process Approach

2000 Mathematics Subject Classification: Primary 60G70, 62F03.We explore a real renewal process representing the successive arrival times of some event (ex.: clinical case of an infectious disease). We wish to test that the first observed events are sporadic, and not emergent. We also compare this distribution to the one got under the independency of standard setting. We finally illustrate this approach by testing on the first observations of a simulation of a slowly emergent phenomenon that this phenomenon is a sporadic one, and we show that the statistic based on the extremal process is much more efficient and robust than the statistic based on the record values

Estimation in a Binomial Stochastic Blockmodel for a Weighted Graph by a Variational Expectation Maximization Algorithm

Stochastic blockmodels have been widely proposed as a probabilistic random graph model for the analysis of networks data as well as for detecting community structure in these networks. In a number of real-world networks, not all ties among nodes have the same weight. Ties among networks nodes are often associated with weights that differentiate them in terms of their strength, intensity, or capacity. In this paper, we provide an inference method through a variational expectation maximization algorithm to estimate the parameters in binomial stochastic blockmodels for weighted networks. To prove the validity of the method and to highlight its main features, we set some applications of the proposed approach by using some simulated data and then some real data sets. Stochastic blockmodels belong to latent classes models. Classes defines a node's clustering. We compare the clustering found through binomial stochastic blockmodels with the ones found fitting a stochastic blockmodel with Poisson distributed edges. Inferred Poisson and binomial stochastic blockmodels mainly differs. Moreover, in our examples, the statistical error is lower for binomial stochastic blockmodels

Risque d'émergence d'une pathologie dans une population (Evaluation à l'aide d'une approche par processus extrême)

LE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocSudocFranceF

Modélisation statistique des évènements rares et en particulier des valeurs extrêmes

National audienceA rare event is an event that has a low probability to occur. Thus, rare events are rarely observed in datasets. As we have a few, or none, observation of these rare events, we face a problem of lack of information, in particular when applying a probabilistic or statistic analysis. Compared to classic events, rare events may correspond to an outside chance, or they can suddenly appear more frequently than expected cause to a change of behaviour corresponding to the emergence of a new phenomena. When this change occurs, we want to detect it as soon as possible. Thus, when a (or several) rare event(s) occurs, the question is: is this a hazard or a change of behaviour? To answer this we first have to study precisely the distribution of rare events due to hazard. These are called extreme values as they are values much greater or smaller than the ones currently observed. I will expose two equivalent methods to model extreme values: the maxima and the excesses methods. Concerning the detection of emergence, I will present a new method based on the study of records

An extremal process approach for sporadic events

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Early detection of emergent events based on an extremal process approach

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Inférence fondée sur la vraisemblance pour des modèles de records

International audiencei n f o a r t i c l e r é s u m é Historique de l'article : Reçu le 17 juin 2017 Accepté après révision le 4 octobre 2017 Disponible sur Internet le 9 octobre 2017 Présenté par le comité de rédaction Dans une série chronologique {X t , t ≥ 1}, X j est un record supérieur si X j > max{ X 1 ,. .. , X j−1 }. Des modèles populaires pour de telles données sont le modèle de Yang–Nevzorov et le modèle à dérive linéaire. Dans cette note, nous présentons la vraisemblance jointe de la suite des records et de leur indicatrice d'occurrence. Cette vraisemblance peut ensuite être utilisée pour estimer les paramètres inconnus des modèles. Elle peut aussi être utilisée pour construire des procédures inférentielles pour la sélection d'un modèle adapté à ces données. © 2017 Académie des sciences. Publié par Elsevier Masson SAS. Cet article est publié en Open Access sous licence CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/). a b s t r a c t In a time series {X t , t ≥ 1}, X j is said to be an upper record if X j > max X 1 ,. .. , X j−1. Some popular models for records are the Yang–Nevzorov and the Linear Drift models. In this note, we introduce for these models the joint likelihood of the record sequence and the indicators of their occurrence. This likelihood can then be used to obtain estimators of the unknown parameters in the models. It can also be used to derive inferential procedures associated with the selection of a proper model for such data

Trend detection for heteroscedastic extremes

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Distribution-free inference in record series

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