Spatio-temporal Functional Regression on Paleo-ecological Data

The influence of climate on biodiversity is an important ecological question. Various theories try to link climate change to allelic richness and therefore to predict the impact of global warming on genetic diversity. We model the relationship between genetic diversity in the European beech forests and curves of temperature and precipitation reconstructed from pollen databases. Our model links the genetic measure to the climate curves through a linear functional regression. The interaction in climate variables is assumed to be bilinear. Since the data are georeferenced, our methodology accounts for the spatial dependence among the observations. The practical issues of these extensions are discussed

Sequential Aggregation of Probabilistic Forecasts -- Applicaton to Wind Speed Ensemble Forecasts

In the field of numerical weather prediction (NWP), the probabilistic distribution of the future state of the atmosphere is sampled with Monte-Carlo-like simulations, called ensembles. These ensembles have deficiencies (such as conditional biases) that can be corrected thanks to statistical post-processing methods. Several ensembles exist and may be corrected with different statistiscal methods. A further step is to combine these raw or post-processed ensembles. The theory of prediction with expert advice allows us to build combination algorithms with theoretical guarantees on the forecast performance. This article adapts this theory to the case of probabilistic forecasts issued as step-wise cumulative distribution functions (CDF). The theory is applied to wind speed forecasting, by combining several raw or post-processed ensembles, considered as CDFs. The second goal of this study is to explore the use of two forecast performance criteria: the Continous ranked probability score (CRPS) and the Jolliffe-Primo test. Comparing the results obtained with both criteria leads to reconsidering the usual way to build skillful probabilistic forecasts, based on the minimization of the CRPS. Minimizing the CRPS does not necessarily produce reliable forecasts according to the Jolliffe-Primo test. The Jolliffe-Primo test generally selects reliable forecasts, but could lead to issuing suboptimal forecasts in terms of CRPS. It is proposed to use both criterion to achieve reliable and skillful probabilistic forecasts.Comment: 38 pages, 7 figure

Testing the independence of maxima: from bivariate vectors to spatial extreme fields

International audienceCharacterizing the behaviour of multivariate or spatial extreme values is of fundamental interest to understand how extreme events tend to occur. In this paper we propose to test for the asymptotic independence of bivariate maxima vectors. Our test statistic is derived from a madogram, a notion classically used in geostatistics to capture spatial structures. The test can be applied to bivariate vectors, and a generalization to the spatial context is proposed. For bivariate vectors, a comparison to the test by Falk and Michel (2006) is conducted through a simulation study. In the spatial case, special attention is paid to pairwise dependence. A multiple test procedure is designed to determine at which lag asymptotic independence takes place. This new procedure is based on the bootstrap distribution of the number of times the null hypothesis is rejected. It is then tested on maxima of three classical spatial models and finally applied to two climate datasets

Spatio-temporal modeling of avalanche frequencies in the French Alps

AbstractAvalanches threaten mountainous regions, and probabilistic long term hazard evaluation is a useful tool for land use planning and the definition of appropriate mitigation measures. This communication focuses on avalanches counts in the French Alps, and investigates their fluctuations in space and time within a Bayesian hierarchical modeling framework.We have at our disposal a 60 year data set covering the whole French Alps. The considered time scale is the winter. The elementary spatial scale is the township. It is small enough to allow information transfer between neighboring paths and large enough to avoid errors in paths localization. Data are standardized with a variable integrating the number of surveyed paths.A hierarchical Poisson-lognormal model appears well-adapted to depict the observation process with such discrete data. The spatial and temporal effects are assumed independent, and they are considered in the latent layer of the model. The temporal trend is modeled with a cubic spline whereas different spatial dependence sub-models are tested. The latter ones work on different types of supports (continuous field and discrete grid), and at different embedded spatial scales. Model inference and predictive sampling are carried out using Markov Chain Monte Carlo simulation methods. The spatial structure explains the larger part of the relative risks. The spatial dependence is visible at the scale of townships, but with a short range. At the larger scale of the massifs, the spatial dependence is weaker.The regional coherence of the results with the number of avalanche releases suggests that we may also search for other spatially structured variables implicated in the magnitude of avalanches that could help transfer information from one path to another

Conditional simulation of a positive random vector subject to max-linear constraints. A geometric perspective

Full text available for free at http://geostats2012.nr.no/pdfs/1748421.pdfInternational audiencePredicting natural phenomena modeled by max-stable random fields with Fréchet margins is not simple because these models do not possess finite first and second order moments. In such situations, a Monte Carlo approach based on conditional simulations can be considered. In this paper we examine a recent algorithm set up by Wang and Stoev to conditionally simulate a max-stable random field with discrete spectrum. Besides presenting this algorithm, we provide it with a geometric interpretation and put emphasis on several implementation details to obviate its combinatorial complexity. Along the way, a number of other critical issues are mentioned that are not often addressed in the current practice of conditional simulations. An illustrative example is given

Sur la réduction des modèles linéaires : analyse de données en automatique

Two state space model reduction methods are studied : aggregation method and the balanced state space representation method.In the case of aggregation a new method of selecting eigenvalues is proposed, wich is both geometrical and sequential. Problems of robustness of aggregation are evoked and resolved in some particular cases. The balanced state space representation is approached by means of contralibility and observability degrees. The notion of perturbability degree is introduced. Then we study the application of those two methods to reduced order compensator design. The two methods are finally applied to the system representing the launch booster Ariane flying.Nous étudions ici deux méthodes de réduction dans l'espace d'état : l'agrégation et la troncature dans la base d'équilibre.Dans le cas de l'agrégation on propose une nouvelle méthode de sélection des valeurs propres qui est à la fois géométrique et séquentielle. Des problèmes de robustesse sont soulevés et résolus dans quelques cas particuliers.La base d'équilibre est abordée par le biais des degrés de commandabilité et d'observabilité. La notion de degré de perturbabilité est introduite.On étudie ensuite l'application de ces deux méthodes à la détermination d'une commande d'ordre réduit.Enfin les deux méthodes sont appliquées au système représentant le lanceur Ariane en vol

Enseignement de statistique pour des ingénieurs des sciences du vivant

International audienc

Sur la reduction des modeles lineaires : analyse de donnees en automatique

SIGLECNRS T 57576 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Editorial to the METMA 2018: Space–time modeling of rare events and environmental risks

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