Zobrazeno 1 - 10
of 259
pro vyhledávání: '"Martino, Sara"'
This work aims to combine two primary meteorological data sources in the Philippines: data from a sparse network of weather stations and outcomes of a numerical weather prediction model. To this end, we propose a data fusion model which is primarily
Externí odkaz:
http://arxiv.org/abs/2404.08533
Modern methods for quantifying and predicting species distribution play a crucial part in biodiversity conservation. Occupancy models are a popular choice for analyzing species occurrence data as they allow to separate the observational error induced
Externí odkaz:
http://arxiv.org/abs/2403.10680
Aiming to deliver improved precipitation simulations for hydrological impact assessment studies, we develop a methodology for modelling and simulating high-dimensional spatial precipitation extremes, focusing on both their marginal distributions and
Externí odkaz:
http://arxiv.org/abs/2307.11390
Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention during the past decades. However, while most researchers are familiar
Externí odkaz:
http://arxiv.org/abs/2303.15240
A successful model for high-dimensional spatial extremes should, in principle, be able to describe both weakening extremal dependence at increasing levels and changes in the type of extremal dependence class as a function of the distance between loca
Externí odkaz:
http://arxiv.org/abs/2210.00760
When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify - in space and time - the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020
Externí odkaz:
http://arxiv.org/abs/2110.15020
A new method is proposed for modelling the yearly maxima of sub-daily precipitation, with the aim of producing spatial maps of return level estimates. Yearly precipitation maxima are modelled using a Bayesian hierarchical model with a latent Gaussian
Externí odkaz:
http://arxiv.org/abs/2105.09062
Publikováno v:
Lobachevskii J. Math. 44(6):2135-2159 (2023)
We derive explicitly the structural properties of the $p$-adic special orthogonal groups in dimension three, for all primes $p$, and, along the way, the two-dimensional case. In particular, starting from the unique definite quadratic form in three di
Externí odkaz:
http://arxiv.org/abs/2104.06228
Autor:
Martino, Sara, Pace, Daniela Silvia, Moro, Stefano, Casoli, Edoardo, Ventura, Daniele, Frachea, Alessandro, Silvestri, Margherita, Arcangeli, Antonella, Giacomini, Giancarlo, Ardizzone, Giandomenico, Lasinio, Giovanna Jona
Presence-only data are a typical occurrence in species distribution modeling. They include the presence locations and no information on the absence. Their modeling usually does not account for detection biases. In this work, we aim to merge three dif
Externí odkaz:
http://arxiv.org/abs/2103.16125
The Integrated Nested Laplace Approximation (INLA) is a deterministic approach to Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate approximation of posterior marginals for the parameters in the models. Recently, me
Externí odkaz:
http://arxiv.org/abs/2103.02721