Homepage Denis Allard
Directeur de Recherche / Senior Researcher
Biostatistics and Spatial Processes (BioSP), INRAE, Avignon
Tel: +33 (0) 4 32 72 21 71 Fax: +33 (0) 4 32 72 21 82 denis.allard@inrae.frhttps://orcid.org/0000000179441906
Member of the Applied Mathematics and Informatics division (MIA) of the French National Research Institute for Agriculture, Food & Environment
News / Bio and Vita / WACSgen / Research / Publications / PhD Students / Teaching / Professional services / Editorial services
 I am the coordinator of the research network on statistics for spatiotemporal data RESSTE (RESeau Statistiques pour Données SpatioTemporelles). RESSTE is funded by the Applied Mathematcis and Informatics division (MIA) of INRA. It gathers more than 60 researches from about 20 research teams in France and abroad. We organize seminars and workshops and we support all sorts of actions actions in view of developping models and methods for analyzing spacetime data. Feel free to contact me if you wish to be on the RESSTE mailing list.
réseau RESSTE network
 The Journal de la Société Française de Statistique recently published a Special Issue Statistics for spatial and spatiotemporal data and RESSTE Network, containing very interesting contributions by several RESSTE network members.
 The paper Anisotropy Models for Spatial Data, Math. Geosc. Volume 48(3): 305–328, doi: 10.1007/s110040159594x., coauthered with Rachid Senoussi from BioSP and Emilio Porcu from University of Newcastle was awarded best 2016 paper by the Journal Mathematical Geoscience. The paper offers a full characterization of anisotropic variograms, in terms of both regularity and range. It is first shown that, if the regularity parameter is a continuous function of direction, it must necessarily be constant, whereas the scale parameter can vary in a continuous or discontinuous fashion with direction. As a second result, it is then established that all valid anisotropies for the range parameter can be represented as a directional mixture of zonal anisotropies. This representation makes it possible to build a very large class of anisotropic variograms, far more flexible than the classical anisotropies. A turning band algorithm for the simulation of Gaussian anisotropic random fields, derived from the mixture representation, is then presented and illustrated.
Recent conferences I (co) organized and/or I have been involved with:
 METMA 2018 is the ninth of a series of workshops on the topic of SpatioTemporal Modelling, which have been held every two years since 17 years. We are proud to organize the first "French" edition of this event in Montpellier. This workshop aims to promote the development and application of spatial, temporal, and mainly spatiotemporal statistical methods in different fields related to the environment. It seeks to bring together practitioners and researchers of different areas and countries all over the world.The scientific program features sessions covering topics on the latest advancements in theory, methods and applications.
 L’Université d’Avignon (UAVP) accueillera les Journées de Statistique 2017 du 29 mai au 2 juin sur le campus Hannah Arendt (anciennement SainteMarthe) au centreville d’Avignon. L'événement est coorganisé par le laboratoire de mathématique d'Avignon, BioSP, le laboratoire d'Informatique d'Avignon et par l'UMR ESPACE.
 The 2015 edition of the Spatial Statistics Conference took place in Avignon, 9  12 June, 2015. It was cochaired by Denis Allard (BioSP, INRA) and Alfred Stein (ITC). It was sponsored by the Applied Mathematics and Computer Science division of INRA.
 BioSP hosted the Workshop on Stochastic Weather Generators from 17  19 september, 2014. This workshop brought together a wide range of researchers, practitioners, and graduate students whose work is related to the stochastic modelling of meteorological variables and stochastic weather generators. Presentations can be found here.
 The 9th edition of the FrenchDanish Workshop took place in May 2012 in Avignon, France. It was jointly organized by the Biostatistics and Spatial Processes research unit (INRA) and the Dpt. of Mathematics, LANLG (University of Avignon). It was devoted to spatial statistics and image analysis and their applications in biology (agriculture, aquaculture, ecology, economy, environment, health, medicine, ...). Presentations can be found here.
Denis Allard obtained his MSc and PhD in Geostatistics from the Ecole des Mines de Paris, in Fontainebleau, France. He has been Assistant Professor at the Statistics Department, University of Washington (Seattle, U.S.A.) and Senior Geostatisticians for BP. In 1996 he joined the French National Institute for Agricultural Research (INRA) in Avignon, France, which he found to be an excellent place for doing research and which he never left. From 2005 to 2011 he has been the head of the BioSP (Biostatistics and Spatial Processes) group.
His research covers a wide range of topics in geostatistics and spatial statistics for modeling and analyzing spatiotemporal data, with applications in geosciences, environment and climate sciences. Recent theoretical contributions include the aggregation of probabilities in geoscience, efficient geostatistical simulation techniques, new classes of multivariate spacetime crosscovariance functions, full characterization of anisotopy for random fields and skewnormal distributions for spatial data.
Recent areas of applications include
 rainfall modelling, stochastic weather generators, and the use of climate variables in impact models in context of Climate Change;
 estimation of vegetation indices using remote sensors such as satellite images, photographies and LIDAR measurements;
 geostatistical modeling of indicators, probabilities and connectivity in geoscience.
He has been Associate Editor for Computing ans Statistics (20062017) and for Mathematical Geosciences (20152017). He is member of the editorial board of Spatial Statistics since 2012.
He is member of the steering committee of the "Adaptation of Agriculture and Forest to Climate Change" INRA metaprogram. Since September 2017, he is charge of Innovation, Partnership and Transfer on Digital Agriculture for INRA.
Vita
1993 PhD in geostatistics, Paris School of Mines / Centre de Géostatistique de l'Ecole des Mines de Paris, maintenant Equipe de Géostatistique du Centre de Géoscience de Mines ParisTech
1994  1995 Visting Assistant Professor, Department of Statistics, University of Washington, Seattle (WA, USA)
1995  1996 Geostatistician, BP Exploration  Subsurface Technology, Londres
since 1996 Researcher, Biostatistics and Spatial Processes (BioSP), INRA, Avignon
2007 Habilitation à Diriger les Recherches (Université Montpellier II)
2008 Senior Researcher, BioSP, INRA / Directeur de Recherche INRA
2005  2011 Head of BioSP / Directeur de l'Unité Biostatistique et Processus Spatiaux
2016 Visiting Professor at Dipartimento di Scienze Ambietali, Informatica e Statistica (DAIS), University Ca'Foscari, Venezia.
Since 2016 Member of the steering committee of the INRA metaprogramme "Adaptation of Agriculture and Forest to Climate Change"
Since 2017 Within INRA, I am in charge of Innovation, Partnership and Transfer for "Digital Agriculture"
Full vita in English here
WACSgen is a singlesite, stationary multivariate weather generator for daily climate variables based on weatherstates that uses a Markov chain for modeling the succession of (an unlimited number of) weather states. Conditionally to the weather states, the multivariate variables are modeled using the family of Complete skewnormal distributions. It is described in Flecher et~al. (2010).
Version WACSgen 1.0 is now avaliable to download. Here is the zip file of the R pacakge WACS. Simply download in your owkring directory and install with the usual install command install.packages(,) or from the Rstudio tool. WACS is also available as a package on the RCRAN repository. Follow this link.
A user guide with a full description of the model, methods and algorithms is accessible here. Feel free to use WCASgen and to contact me for complementary information. Do not forget to make proper reference to WACSgen and the original paper Flecher et~al. (2010).
Simulating spacetime random fields with nonseparable Gneitingtype covariance functions [with Xavier Emery, Céline Lacaux and Christian Lantuéjoul]
We propose two algorithms to simulate spacetime Gaussian random fields with a covariance function belonging to an extended Gneiting class, the definition of which depends on a completely monotone function associated with the spatial structure and a conditionally negative definite function associated with the temporal structure. In both cases, the simulated random field is constructed as a weighted sum of cosine waves, with a Gaussian spatial frequency vector and a uniform phase. The difference lies in the way to handle the temporal component. The first algorithm relies on a spectral decomposition in order to simulate a temporal frequency conditional upon the spatial one, while in the second algorithm the temporal frequency is replaced by an intrinsic random field whose variogram is proportional to the conditionally negative definite function associated with the temporal structure. Both algorithms are scalable as their computational cost is proportional to the number of spacetime locations, which may be unevenly spaced in space and/or in time. They are illustrated and validated through synthetic examples. arXiv: 1912.02026
A general framework for SPDEbased stationary random fields [with R. Carizo Vergara and N. Desassis]
The paper presents theoretical advances in the application of the Stochastic Partial Differential Equation (SPDE) approach in geostatistics. We show a general approach to construct stationary models related to a wide class of SPDEs, with applications to spatiotemporal models having nontrivial properties. Within the framework of Generalized Random Fields, a criterion for existence and uniqueness of stationary solutions for a wide class of linear SPDEs is proposed and proven. Their covariance are then obtained through their associated spectral measure. We also present a result that relates the covariance in the case of a White Noise source term with that of a generic case through convolution. Using these results, we obtain a variety of SPDEbased stationary random fields. In particular, wellknown results regarding the Matérn Model and models with Markovian behavior are recovered. A new relationship between the Stein model and a particular SPDE is obtained. New spatiotemporal models obtained from evolution SPDEs of arbitrary temporal derivative order are then obtained, for which properties of separability and symmetry can easily be controlled. We also obtain results concerning stationary solutions for physically inspired models, such as solutions for the heat equation, the advectiondiffusion equation, some Langevin's equations and the wave equation. arXiv:1806.04999
Halftapering strategy for conditional simulation with large datasets [with D. Marcotte]
Gaussian conditional realizations are routinely used for risk assessment and planning in a variety of Earth sciences applications. Conditional realizations can be obtained by first creating unconditional realizations that are then postconditioned by kriging. Many efficient algorithms are available for the first step, so the bottleneck resides in the second step. Instead of doing the conditional simulations with the desired covariance (F approach) or with a tapered covariance (T approach), we propose to use the taper covariance only in the conditioning step (HalfTaper or HT approach). This enables to speed up the computations and to reduce memory requirements for the conditioning step but also to keep the right short scale variations in the realizations. A criterion based on mean square error of the simulation is derived to help anticipate the similarity of HT to F. Moreover, an index is used to predict the sparsity of the kriging matrix for the conditioning step. Some guides for the choice of the taper function are discussed. The distributions of a series of 1D, 2D and 3D scalar response functions are compared for F, T and HT approaches. The distributions obtained indicate a much better similarity to F with HT than with T. A preprint is avalaible here. Publication in SERRA is here.
Means and covariance functions for spatial compositional data: an axiomatic approach [with T. Marchant]
Our work focuses on the characterization of the central tendency of a sample of compositional data. It provides new results about theoretical properties of means and covariance functions for compositional data, with an axiomatic perspective. Original results that shed new light on the geostatistical modeling of compositional data are presented.As a first result, it is shown that the weighted arithmetic mean is the only central tendency characteristic verifying a small set of axioms, namely reflexivity and marginal stability. Moreover, the weights must be identical for all components of the compositional vector.This result has deep consequences on the spatial multivariate covariance modeling of compositional data. In a geostatistical setting,it is shown as a second result that the proportional model of covariance functions (i.e. the product of a covariance matrix and a single correlation function) is the only model that provides identical kriging for all components of the compositional data. As a consequence of these two results, the proportional model of covariance function is the only covariance model compatible with reflexivity and marginal stability. A preprint can be found here. Publication in Mathematical Geosciences is here.
Multivariate spacetime models [with M. Bourotte and E. Porcu]
Multivariate spacetime data are increasingly recorded in various scientific disciplines. When analyzing these data, one of the key issue is to describe the multivariate spacetime dependencies. In a Gaussian framework, this necessitates to propose relevant models for multivariate spacetime covariance functions, mathematically described as matrixvalued covariance functions for which nonnegative definiteness must be ensured. A new flexible parametric class of crosscovariance functions for multivariate spacetime Gaussian random fields has been proposed where spacetime components belong to the (univariate) Gneiting class of spacetime covariance functions, with Matern or Cauchy covariance functions in the spatial dimensions. In this class, the smoothness and the scale parameters can be different for each variable. Sufficient conditions are provided, ensuring that this model is a valid matrixvalued covariance functionfor multivariate spacetime random fields. Through a simulation study, it is shown that the parameters of this model can be efficiently estimated using weighted pairwise likelihood, which belongs to class of composite likelihood methods. A preprint is available here. Publication in Spatial Statistics is here.
Variograms for anisotropic random fields [with R. Senoussi and E. Porcu]
The question of building useful and valid models of anisotropic variograms for spatial data that go beyond classical anisotropy models (geometric and zonal models of anisotropy) is rarely addressed. In Allard, Senoussi and Porcu (Math. Geosciences) it is shown that if the regularity parameter is a continuous function of the direction, it must necessarily be constant. Instead, the scale parameter can vary in a continuous or discontinuous fashion with the direction according to a directional mixture representation, which allows to build a very large class of anisotropy models. A turning band algorithm for the simulation of Gaussian anisotropic processes, obtained from the mixture representation, is also presented.
Stochastic Weather Generators [with P. Naveau, P. Ailliot, V. Monbet, M. Bourotte]
A recurrent issue encountered in impact studies is to provide fast and realistic (in a distributional sense) simulations of atmospheric variables like temperatures, precipitation and winds at a few specific locations and at daily or hourly temporal scales. This stochastic inquiry leads to a large variety of socalled Stochastic Weather Generators (SWG) in the hydrological and weather literature. A concise and uptodate review paper on Weatherstates stochastic Weather Generators is available in Ailliot, Allard, Monbet and Naveau (2015).
To simulate multivariate daily time series (minimum and maximum temperatures, global radiation, wind speed and precipitation intensity) at a single site, WACSGen, a Weatherstate Approach with conditionally multivariate Closed Skewnormal distribution is proposed in Flecher, Naveau, Allard, Brisson (WRR, 2010). WACSGen is able to accurately reproduce the statistical properties of these five variables, including time dependence. It takes advantage of two elements. First, the classical wet and dry days dichotomy used in most past weather generators is generalized to multiple weather states using clustering techniques. The transitions among weather states are modeled by a first order Markov chain. Secondly, the vector of our five daily variables of interest is sampled, conditionally on these weather states, from a closed skewnormal distribution, thus allowing to handle nonsymmetric behaviors. WACSGen is coded in R, and is available upon request by emailing me.
Allard and Bourotte (2014) considers the problem disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process. Current researches aim at proposing relevant models for multisite, multivariate Stochastic Weather Generators.
Combining indicator probabilities [with D. D'Or, R. Froidevaux, A. Communian, P. Renard]
The need of combining in a probabilistic framework different sources of information is a frequent task in geoscience. For example, the probability of occurrence of a certain lithofacies at a given location can easily be computed conditionally on the values observed at other sources of information (sample observations, geophysics, remote sensing, training images). The problem of aggregating these different conditional probability distributions into a single conditional distribution arises as an approximation to the inaccessible genuine conditional probability given all information. Allard, Communian and Renard (2012) makes a formal review of most aggregation methods with a particular focus on their mathematical properties. Exact relationships relating the different methods is emphasized. The case of events with more than 2 possible outcomes is treated in details. It is shown that in this case, equivalence between different aggregation formulas is lost. It is proved that the loglinear pooling formulas with parameters estimated from maximum likelihood are calibrated. These results are illustrated on simulations from two common stochastic models for earth science: the truncated Gaussian model and the Boolean model.
When considering the problem of the spatial prediction of a categorical variable given a set of observations at surrounding locations, a useful approximation of the conditional probability of observing a category at a location is obtained with a particular maximum entropy principle. It leads to a simple combination of sums and products of univariate and bivariate probabilities. This prediction equation can be used for categorical estimation or categorical simulation. In Allard, D'Or and Froideveaux (2011), connections are made to earlier work on prediction of categorical variables. In particular, it is a parameter free, suboptimal, special case of loglinear pooling.
Skew normal random fields [with P. Naveau]
Skewness is often present in a wide range of environmental problems, and modelling it in the spatial context remains a challenging problem. In Allard and Naveau (2007), a new family of skewed random fields based on the multivariate closed skewnormal distribution is proposed. Such fields can be written as the sum of two independent fields; one Gaussian and the other truncated Gaussian. This model contains very few parameters while still incorporating the classical spatial structures used in geostatistics. Crucially, a high degree of skewness can be induced through the use of a single skewness parameter. It is thus possible to compute the first and secondorder moments of our skewed fields, as well as deriving the properties of the sample variogram and covariance. This leads to a method of moments algorithm to estimate the parameters.
Zones of Abrupt Changes [with Edith Gabriel and J.N. Bacro]
Estimating the zones where a variable under study changes abruptly is a problem encountered in many biological, ecological, agricultural or environmental applications. In Gabriel, Allard and Bacro (2011), a method is proposed for detecting the zones where a spatially correlated variable irregularly sampled in the plane changes abruptly. The general model is that under the null hypothesis the variable is the realization of a stationary Gaussian process with constant expectation. The alternative is that the mean function is discontinuous on some curves in the plane. The general approach is a global aggregation of local tests of the hypothesis of a local constant mean vs. the alternative of the existence of a discontinuity. The theory that links the local and global levels is based on asymptotic distributions of excursion sets of nonstationary khi^{2} fields. It is thus possible to control the global type I error and to simultaneously estimate the covariance function and the ZACs in the case of an unknown mean. This method is easy to use, to visualise and to interpret. An R set of functions, detecZAC, can be downloaded from Edith Gabriel's homepage.
CART algorithm for spatial data [with Liliane Bel and Avner BarHen]
Classification And Regression Trees (CART) assume independent samples to compute classification rules. This assumption is very practical for estimating quantities involved in the algorithm and for assessing asymptotic properties of estimators. Unfortunately, in most environmental or ecological applications, the data under study present some amount of spatial correlation. When the sampling scheme is very irregular, a direct application of supervised classification algorithms leads to biased discriminant rules due, for example, to the possible oversampling of some areas. In Bel, Allard, Laurent, Cheddadi and BarHen (2009), two approaches for taking this spatial dependence into account are considered. The first one takes into account the irregularity of the sampling by weighting the data according to their spatial pattern using two existing methods based on Voronoï tessellation and regular grid, and one original method based on kriging. The second one uses spatial estimates of the quantities involved in the construction of the discriminant rule at each step of the algorithm.
Full list of publications is here
HDR Thesis can be found here
Book edition
Monestiez, P., Allard, D., Froidevaux, R. (2001) geoENV III Geostatistics for Environmental Applications. Kluwer Academic Publishers, Dordrecht, 540p.
Book reviews (avalaible upon request)
J.P. Chilès, P. Delfiner: Geostatistics: Modeling Spatial Uncertainty 2nd Edition. Wiley, 2012. Mathematical Geosciences, 2012. doi:10.1007/s110040129429y
A.E. Gelfand, P.J. Diggle, M. Fuentes, P. Guttorp (eds.): Handbook of spatial statistics, Chapman & Hall/CRC, Statistics and Computing, 2010. doi:10.1007/s1122201092112
Some recent (and not so recent anymore) publications
François B., Vrac M., Cannon A.J., Robin R., Allard D. Multivariate bias corrections of climate simulations: Which benefits for which losses? Submitted.
Opitz T., Allard D., Mariethoz G. Nonparametric resampling with extremes. Submitted, in revision.
Allard, D. Emery X., Lacaux, C., Lantuéjoul, C. (2019) Simulating spacetime random fields with nonseparable Gneitingtype covariance functions. Submitted, arXiv: 1912.02026
Soma M., Pimont F., Allard D., Fournier R., Dupuy, J.L. Mitigating occlusion effects in LAD estimates from Terrestrial LiDAR through a specific kriging method. Submitted, in revision.
Cuevas, F., Porcu, E., Allard, D. (2018) Fast and exact simulation of isotropic Gaussian random fields defined on the sphere cross time. Statistics an Computing (accepted for publications) . arXiv:1807.04145
Carrizo Vergara R., Allard, D., Desassis, N. (2018) A general framework for SPDEbased stationary random field. arXiv:1806.04999
Pimont, F., Allard, D., Soma M. Dupuy, JL (2018) Estimators and confidence intervals for plant area density at voxel scale with TLiDAR. Remote Sensing of Environment, 215, 343370. doi.org/10.1016/j.rse.2018.06.024
Benoit L., Allard D., Mariethoz, G. (2018) Stochastic Rainfall Modelling at Sub‐Kilometer Scale. Water Resources Research, 54, 6, 41084130 doi.org/10.1029/2018WR022817
Marcotte, D. and Allard, D. (2018) Gibbs sampling on large lattice with GMRF. Computers and Geosciences, 111, 190199. doi:10.1016/j.cageo.2017.11.012.
Allard, D. and Marchant, T. (2018) Means and covariance functions for spatial compositional data: an axiomatic approach, Mathematical Geosciences, 50(3), 299315; doi: 10.1007/s110040179713y. Manuscript accessible here.
with RESSTE network (2017). Analyzing spatiotemporal data with R: Everything you always wanted to know  but were afraid to ask. Journal de la Société Française de Statistique, 158(3), 124158. Supplementary material is here
Csilléry, K., Kunstler G., Courbaud, B., Allard, D., Lassegues, P., Haslinger, K., Gardiner, G. Coupled effects of windstorms and drought on tree mortality across 115 forest stands from the Western Alps and the Jura mountains, Global Change Biology 23(12) 50925107. DOI : 10.1111/gcb.13773
Marcotte, D. and Allard, D. (2017) Halftapering strategy for conditional simulation with large datasets, Stochastic Environmental Research and Risk Assessment, 32(1), 279294. doi: 10.1007/s004770171386z. Manuscript accessible here.
Bourotte M., Allard, D. and Porcu, E. (2016) A Flexible Class of Nonseparable CrossCovariance Functions for Multivariate SpaceTime Data, Spatial Statistics, 18(A), 125146. doi: 10.1016/j.spasta.2016.02.004. (manusript: ArXiv 1510.07840)
Zaytsev, V. BIver, P., Wackernagel, H. and Allard, D. (2016) ChangeofSupport Models on Irregular Grids for Geostatistical Simulation, Mathematical Geosciences, 48(4): 353369. doi: 10.1007/s110040159614x.
Allard, D. Senoussi, R., Porcu, E. (2016) Anisotropy models for spatial data. Mathematical Geosciences, 48(3): 305328. doi: 10.1007/s110040159594x. Preprint accessible here.
Ailliot P., Allard, D., Monbet V. and Naveau, P. (2015) Stochastic weather generators: an overview of weather type models. Journal de la Société Française de Statistiques, 156(1), 101103. Paper accessible here.
Allard, D., Bourotte, M. (2015) Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process. Stochastic Environmental Research and Risk Assesment, 29(2), 453462, doi: 10.1007/s0047701409134. Preprint accessible here.
Allard, D., LopezLozano, R. and Baret, F. (2013) Modeling forest canopies with a hierarchical multiring Boolean model for estimating Leaf Area Index. Spatial Statistics, 5, 4256. doi:10.1016/j.spasta.2013.04.007. Preprint accessible here.
Renard, P. and Allard, D. (2013) Connectivity metrics for subsurface flow and transport. Advances in Water Resources, 51, 168196. doi:10.1016/j.advwatres.2011.12.001 Preprint accesstible here.
Girard, R. and Allard, D. (2013) Spatiotemporal propagation of wind power prediction errors. Wind Energy,16, 9991012. doi:10.1002/we.1527
Allard, D. Soubeyrand, S. (2012) Skewnormality for climatic data and dispersal models for plant epidemiology: when application fields drive spatial statistics. Spatial Statistics, 1, 5064. doi: 10.1016/j.spasta.2012.03.001. Preprint accessible here.
Allard, D., Communian, A. and Renard, P. (2012) Probability aggregation methods in geoscience. Mathematical Geosciences, 44: 545581. doi: 10.1007/s1100401293963. Preprint accessible here.
Allard, D. (2012) Modeling spatial and spatiotemporal non Gaussian processes. In SpaceTime Processes and Challenges Related to Environmental Problem,
Eds. Porcu, E., Montero, J.M. and Schlather M., Lecture Notes in Statistics, Vol. 207, Springer. pp. 141164. doi: 10.1007/9783642170867_7
Allard, D., D'Or, D. and Froidevaux, R. (2011) Letter to the Editor: Response to W. Li and C. Zhang,
European Journal of Soil Science, 63, 125128. doi: 10.1111/j.13652389.2011.01414.x. Preprint accessible here.
Allard, D., D'Or, D. and Froidevaux, R. (2011) An efficient maximum entropy approach for categorical variable prediction, European Journal of Soil Science, 61, 381293. doi:10.1111/j.13652389.2011.01362.x. preprint accessible here.
Flecher, C. Allard, D. and Naveau P. (2010) Truncated skewnormal distributions: moments, estimation by weighted moments and application to climatic data.Metron  International Journal of Statistics  Special Issue on Skewsymmetric and flexible distributions, LXVIII, 331345. Prepint is accessible here.
Flecher, C., Naveau P., Allard D. and Brisson, N. (2010) A Stochastic Daily Weather Generator for Skewed Data, Water Resource Research, 46, W07519. doi:10.1029/2009WR008098. Preprint accessible here.
Gabriel, E., Allard, D. and Bacro, J.N. (2010) Estimating and testing zones of abrupt change for spatial data, Statistics and Computing, 21, 107120. doi:10.1007/s112220099151x. Preprint accessible here.
Flecher, C., Naveau, Ph. and Allard, D. (2009) Estimating the Closed SkewNormal distributions parameters using weighted moments", Statistics and Probability Letters, 79, 19771984. doi:10.1016/j.spl.2009.06.004. Preprint accessible here.
Bel, L., Allard, D., Laurent, J.M., Cheddadi, R. and BarHen, A. (2009) CART algorithm for spatial data: Application to environmental and ecological data, Computational Statistics and Data Analysis, 53, 30823093. doi:10.1016/j.csda.2008.09.012. Preprint accessible here.
Garrigues, S., Allard, D., Baret, F. Modeling Temporal Changes in Surface Spatial Heterogeneity over an Agricultural site (2008) Remote Sensing of Environment, 112, 588602. doi:10.1016/j.rse.2007.05.014.
Gabriel, E. and Allard, D. Evaluating the Sampling Pattern When Detecting Zones of Abrupt Change (2008) Environmental and Ecological Statistics, 15, 469489. doi:10.1007/s1065100700673. Manuscrit accessible ici.
Gabriel, E., Allard, D., Mary, B. & Guérif, M. (2007) Detecting zones of abrupt change in soil data, with an application to an agricultural field. European Journal of Soil Science, 58, 12731284. doi:10.1111/j.13652389.2007.00920.x.
Garrigues, S., Allard, D., Baret, F. & Morisette, J. (2007) Multivariate Quantification of Landscape Spatial Heterogeneity using Variogram Models. Remote Sensing of Environment, 112, 216230. doi:10.1016/j.rse.2007.04.017
Garrigues, S., Allard, D., & Baret, F. (2007) Using first and second order variograms for characterizing landscape spatial structures from remote sensing imagery, IEEE TGARS, 45, 1823  1834. doi:10.1109/TGRS.2007.894572
Allard D. & Naveau, P. (2007) A new spatial skewnormal random field model, Communications in Statistics, 36, 1821  1834. doi:10.1080/03610920601126290. Manuscrit accessible ici.
Allard D., Froidevaux R. & Biver, P. (2006) Conditional Simulation of MultiType Non Stationary Markov Object Models Respecting Specified Proportions, Mathematical Geology, 38, 959986. doi:10.1007/s1100400690575. Manuscit accessible ici.
Allard, D. & Gabriel, E. (2007), Détection de zones de changement abrupts pour des variables non permanentes du sol: vers la définition de zones homogènes ?, in Agriculture de Précision, Guérif, M. and King, D., Coords., Editions Quae, Paris, pp. 16576.
Garrigues, S., Allard, D., Baret, F. & Weiss, M. (2006) Influence of the spatial heterogeneity on the non linear Estimation of Leaf Area Index from moderate resolution remote sensing data, Remote Sensing of Environment, 105, 286298. doi:10.1016/jrse.2006.07.013
Magnussen S., Allard D., & Wulder M. (2006) Poisson Voronoï tiling for finding clusters in spatial point patterns, Scan. J. For. Res., 21, 239248. doi:10.1080/02827580600688178
Allard, D. (2006), Validation d'un modèle géostatistique pour l'interpolation : application à un événement pluvieux, in Statistiques Spatiales, Eds. Droesbeke, J.J. et Lejeune, M., Technip, Paris, pp. 403414.
Chilès, J.P. & Allard, D. (2005), Stochastic Simulation of Soil Variation, in Geographic Information Technologies for Environmental SoilLandscape Modelling, Ed. Grunwald, S., CRC Press, Boca Raton, pp. 289321.
Edith Gabriel (20012004) Détection de zones de changement abrupt dans des données spatiales et application à l'agriculture de précision, Univsersity Montpellier II, ED ISS. Cosupervised with M. Guérif, EMMAH, INRA Avignon. Has been Maître de Conférence (Assitant Professor) at Laboratoire d'Analyse Non Linéaire et géométrie d'Avignon, Université d'Avignon. Now Directrice de Recherhe (Senior Research) at BioSP, INRAE.
Sébastien Garrigues (20012004) Hétérogénéité spatiale des surfaces terrestres en télédétection ; caractérisation et influence sur l'estimation des variables biophysiques, ENSAR. cosupervised with F. Baret, EMMAH INRA Avignon. Today at EMMAH, INRA Avignon.
Cédric Flécher (20062009 ) Développement de méthodes statistiques pour la mise au point d'un générateur de climat adapté à l'utilisation des scénarii de changement climatique, University Montpellier II, ED SIBAGHE, cosupervised with Ph. Naveau, LSCE, CNRS and N. Brisson AgroClim, INRA Avignon. Today with InBox, Montréal.
Marc Bourotte (20122016) Modèles et algorithmes pour un générateur de temps spatialisé (SWgen) prenant en compte les valeurs extrêmes. Université d'Avignon. Cosupervised with Liliane Bel, AgroParisTech.
Rocardo Carrizo (2015 2018) Spatiotemporal statistical models from stochastic partial derivative equations. cosupervised with Nicolas Desassis, MinesParisTech.
Environmental data analysis, for the Doctoral Program in Environmental Sciences, Universita Ca'Foscari, Venezia, Italy.
Part I: Introduction; Exploratory data analysis; estimation and hypothsis testing; linear model
Part II: Time series; spatial statistics; geostatistics
Atelier Statistique de la SFdS: Introduction aux méthodes spatiales et spatiotemporelles, 23 et 24 juin 2016. Présentations "Géostatistique multivariée", "Géostatistique Spatiotemporelle" et script R.
Cargèse Fall School on "statistical and mathematical tools for climate extremes", November 2015. Slides on Stochastic Weather Generators are here. Compressed directory for exercises is here
Journées R pour la fédération ECCOREV Scripts R pour le TD
Pratiques des statistiques paramétriques. Séquence I: statistique inférentielle Transparents d'introduction; Transparents Inférence et Tests; Jeu de données du Jura Suisse Script R pour le TD
Toledo Spring School on Advances And Challenges In Spacetime Modelling Of Natural Events. Introduction to NonGaussian Random Fields: a Journey Beyond Gaussianity. Slides
Statistiques Spatiales : introduction à la géostatistique (20 h), M2 Biostatistique, Université Montpellier II.
Probabilité et Statistiques (27 h), Centre de Recherche et d'Enseignement en Informatique, Université d'Avignon. Polycopié du cours.
Processus Stochastiques (40 h),Centre de Recherche et d'Enseignement en Informatique. Université d'Avignon
 2017  now In charge of Innovation, Partnership and Transfer for INRA on Digital Agriculture
 2017 49emes Journées de Statistiques, Avignon, Cochair
 2016  now Member of the "Adaptation of Agriculture and Forest to Climate Change" INRA metaprogram
 2015 Spatial Statistics 2015 conference in Avignon, June 912, Cochair
 2010 Scientific committee, 42^{emes} Journées de Statistique
 2008  2010 Scientfic committee, Université d'Avignon
 2005  Now Member of the Board, Environment Group, French Statitsical Society / Membre du bureau et trésorier du groupe environnement de la SFdS
 2000 Cochair, Geostatistics for Environment, geoENV III, Avignon
 1999  2006 Scientfic Committee, Applied Mathematics and Computer Science division / département Mathématiques et Informatique Appliquées, INRA
 2003  2006 Commission Scientifique Spécialisée Mathématique, Bioinformatique, Intelligence Artificielle, INRA
 2000  2003 Conseil scientifique du Centre d'Avignon, INRA
 2001  Now External Scientific Adviser, EphesiaConsult

2017  Ongoing Member of the Scientific Committee, MeilleursAgents
 2015  2018 Associate Editor, Mathematical Geosciences
 2012  Ongoing Editorial Board, Spatial Statistics
 2006  2018 Associate Editor, Computing and Statistics
 2011  Ongoing Scientific Comiittee, conference series Spatial Statistics
 2000  2016 Scientific Comiittee for the conference series Geostatistics for Environment
Reviewers for Acta Mechanica, Annals of Statistics, Annals of Applied Statistics, Biometrics, Biometrika, Chilean Journal of Statistics, Climate Dynamics, Climate Research, Communications in Statistics  Theory and Methods, Computational Statistics and Data Analysis, Computer and Geoscience, Environmental Modelling & Software, Environmental Pollution, European Journal of Soil Science, IEEE Transactions in Geosciences and Remote Sensing, IEEE Transactions on Power Systems, International Journal of Climatology, Journal of African Earth Sciences, Journal of Agricultural, Biological, and Environmental Statistics, Journal of Computational and Applied Mathematics, Journal of Multivariate Analysis, Journal of the Royal Society Interface, Journal of the Royal Statistical Society, Journal for Stochastic Environmental Research and Risk Assessment, Operations Research, Physics Letters A, Probabilistic Engineering Mechanics, Statistics and Probability Letters, Water Resources Research, etc.