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The AOPN tomatoes and cucumbers carries the DEPHY Serre project with the objective of zero pesticides for these two major vegetables in a soilless system while maintaining economic and environmental performances. The realization of a survey at the start of the project, performed in each production area, identified the mains pests encountered for these crops. Analytic experimentations occurred in two experimental stations, aiming to identify bioproducts of interest as an alternative to chemical pesticides. In parallel, the implementation of monitoring systems was set up among 5 pilot sites (3 for tomatoes and 2 for cucumbers). This stage aimed to validate the results obtained in the experimental stations and to perform a systemic approach by integrating several levers with a global strategy of crop protection. The conducted trials in the stations identified several biocontrol products against tomatoes botrytis (fungi, bacteria or antagonist yeast), and powdery mildew for both crops (drying products which have a direct action against the pathogen or products stimulating the plant defense system). On the pilot sites, the trials enabled to tune the global protection strategies set up in tomato crops with a focus against whiteflies by combining several technics: releasing periods of Macrolophus regarding the crop calendar, interest of feeding at the start of the season, interest of reduced deleafing in release areas, use of biocontrol products, yellow sticky traps for mass trapping, aspiration around the plant's head, etc., The AOPN tomatoes and cucumbers carries the DEPHY Serre project with the objective of zero pesticides for these two major vegetables in a soilless system while maintaining economic and environmental performances. The realization of a survey at the start of the project, performed in each production area, identified the mains pests encountered for these crops. Analytic experimentations occurred in two experimental stations, aiming to identify bioproducts of interest as an alternative to chemical pesticides. In parallel, the implementation of monitoring systems was set up among 5 pilot sites (3 for tomatoes and 2 for cucumbers). This stage aimed to validate the results obtained in the experimental stations and to perform a systemic approach by integrating several levers with a global strategy of crop protection. The conducted trials in the stations identified several biocontrol products against tomatoes botrytis (fungi, bacteria or antagonist yeast), and powdery mildew for both crops (drying products which have a direct [br/] action against the pathogen or products stimulating the plant defense system). On the pilot sites, the trials enabled to tune the global protection strategies set up in tomato crops with a focus against whiteflies by combining several technics: releasing periods of Macrolophus regarding the crop calendar, interest of feeding at the start of the season, interest of reduced deleafing in release areas, use of biocontrol products, yellow sticky traps for mass trapping, aspiration around the plant's head, etc.

In this paper, we discuss the application of extreme value theory in the context of stationary $��$-mixing sequences that belong to the Fr��chet domain of attraction. In particular, we propose a methodology to construct bias-corrected tail estimators. Our approach is based on the combination of two estimators for the extreme value index to cancel the bias. The resulting estimator is used to estimate an extreme quantile. In a simulation study, we outline the performance of our proposals that we compare to alternative estimators recently introduced in the literature. Also, we compute the asymptotic variance in specific examples when possible. Our methodology is applied to two datasets on finance and environment.

The estimation of optimal support boundaries under the monotonicity constraint is relatively unexplored and still in full development. This article examines a new extreme-value based model which provides a valid alternative for complete envelopment frontier models that often suffer from lack of precision, and for purely stochastic ones that are known to be sensitive to model misspecification. We provide different motivating applications including the estimation of the minimal cost in production activity and the assessment of the reliability of nuclear reactors.

In 1985 Hosking et al. estimated with the so-called Probability-Weighted Moments (PWM) method the parameters of the Generalized Extreme Value (GEV) distribution, the latter being classically fitted to maxima of sequences of independent and identically distributed random variables. Their approach is still very popular in hydrology and climatology because of its conceptual simplicity, its easy implementation and its good performance for most distributions encountered in geosciences. Its main drawback resides in its limitations when applied to strong heavy-tailed densities. Whenever the GEV shape parameter is larger than 0.5, the asymptotic properties of the PWMs cannot be derived and consequently, asymptotic confidence intervals cannot be obtained. To broaden the validity domain of the PWM approach, we take advantage of a recent extension of PWM to a larger class of moments, called Generalized PWM (GPWM). This allows us to derive the asymptotic properties of our estimators for larger values of the shape parameter. The performance of our approach is illustrated by studying simulations of small, medium and large GEV samples. Comparisons with other GEV estimation techniques used in hydrology and climatology are performed., REVSTAT-Statistical Journal, Vol. 6 No. 1 (2008): REVSTAT-Statistical Journal

International audience; We propose a new method that could be part of a warning system for the early detection of time clusters applied to public health surveillance data. This method is based on the extreme value theory (EVT). To any new count of a particular infection reported to a surveillance system, we associate a return period that corresponds to the time that we expect to be able to see again such a level. If such a level is reached, an alarm is generated. Although standard EVT is only defined in the context of continuous observations, our approach allows to handle the case of discrete observations occurring in the public health surveillance framework. Moreover, it applies without any assumption on the underlying unknown distribution function. The performance of our method is assessed on an extensive simulation study and is illustrated on real data from Salmonella surveillance in France.

The estimation of optimal support boundaries under the monotonicity constraint is relatively unexplored and still in full development. This article examines a new extreme-value based model which provides a valid alternative for completely envelopment frontier models that often super from lack of precision, and for purely stochastic ones that are known to be sensitive to model misspecification. We provide different motivating applications including the estimation of the minimal cost in production activity and the assessment of the reliability of nuclear reactors.