3 citations · 5 across the 9 of their papers we have counts for
13 papers
A Two-Step Spatio-Temporal Framework for Turbine-Height Wind Estimation at Unmonitored Sites from Sparse Meteorological Data
Eamonn Organ, Maeve Upton, Denis Allard +2
Accurate estimates of wind speeds at wind turbine hub heights are crucial for both wind resource assessment and day-to-day management of electricity grids with high renewable penet…
The SPDE approach for spatio-temporal datasets with advection and diffusion
Lucia Clarotto, Denis Allard, Thomas Romary +1
In the task of predicting spatio-temporal fields in environmental science using statistical methods, introducing statistical models inspired by the physics of the underlying phenom…
Geostatistics for large datasets on Riemannian manifolds: a matrix-free approach
Mike Pereira, Nicolas Desassis, Denis Allard
Large or very large spatial (and spatio-temporal) datasets have become common place in many environmental and climate studies. These data are often collected in non-Euclidean space…
Spatial statistics and stochastic partial differential equations: a mechanistic viewpoint
Lionel Roques, Denis Allard, Samuel Soubeyrand
The Stochastic Partial Differential Equation (SPDE) approach, now commonly used in spatial statistics to construct Gaussian random fields, is revisited from a mechanistic perspecti…
A new class of -transformations for the spatial analysis of Compositional Data
Lucia Clarotto, Denis Allard, Alessandra Menafoglio
Georeferenced compositional data are prominent in many scientific fields and in spatial statistics. This work addresses the problem of proposing models and methods to analyze and p…
Modeling and simulating depositional sequences using latent Gaussian random fields
Denis Allard, Paolo Fabbri, Carlo Gaetan
Simulating a depositional (or stratigraphic) sequence conditionally on borehole data is a long-standing problem in hydrogeology and in petroleum geostatistics. This paper presents…
Semi-parametric resampling with extremes
Thomas Opitz, Denis Allard, Grégoire Mariéthoz
Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features such as spatial patterns from observed datas…
Simulating space-time random fields with nonseparable Gneiting-type covariance functions
Denis Allard, Xavier Emery, Céline Lacaux +1
Two algorithms are proposed to simulate space-time Gaussian random fields with a covariance function belonging to an extended Gneiting class, the definition of which depends on a c…
A general framework for SPDE-based stationary random fields
Ricardo Carrizo Vergara, Denis Allard, Nicolas Desassis
This paper presents theoretical advances in the application of the Stochastic Partial Differential Equation (SPDE) approach in geostatistics. We show a general approach to construc…
Fast and exact simulation of isotropic Gaussian random fields on and
Francisco Cuevas, Emilio Porcu, Denis Allard
We provide a method for fast and exact simulation of Gaussian random fields on spheres having isotropic covariance functions. The method proposed is then extended to Gaussian rando…
Half-tapering strategy for conditional simulation with large datasets
Denis Marcotte, Denis Allard
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…
Means and covariance functions for geostatistical compositional data: an axiomatic approach
Denis Allard, Thierry Marchant
This 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 fu…
A Flexible Class of Non-separable Cross-Covariance Functions for Multivariate Space-Time Data
Marc Bourotte, Denis Allard, Emilio Porcu
Multivariate space-time data are increasingly available in various scientific disciplines. When analyzing these data, one of the key issues is to describe the multivariate space-ti…