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Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women
(Oxford University Press, 2021)
Artículo / Artikulua,
Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for studying multivariate responses are less commonly used mainly due to the ...
High-dimensional order-free multivariate spatial disease mapping
(Springer, 2023)
Artículo / Artikulua,
Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains
limited due to difficulties in implementation and computational burden. These problems are ...
Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns
(SAGE, 2019)
Artículo / Artikulua,
In this paper age-space-time models based on one and two-dimensional P-splines with
B-spline bases are proposed for smoothing mortality rates, where both xed relative scale
and scale invariant two-dimensional penalties ...
Machine learning procedures for daily interpolation of rainfall in Navarre (Spain)
(Springer, 2023)
Capítulo de libro / Liburuen kapitulua,
Kriging is by far the most well known and widely used statistical method
for interpolating data in spatial random fields. The main reason is that it provides
the best linear unbiased predictor and it is an exact interpolator ...
Detecting change-points in the time series of surfaces occupied by pre-defined NDVI categories in continental Spain from 1981 to 2015
(Springer, 2018)
Capítulo de libro / Liburuen kapitulua,
The free access to satellite images since more than 40 years ago
has provoked a rapid increase of multitemporal derived information of remote
sensing data that should be summarized and analyzed for future inferences. ...
Scalable Bayesian modeling for smoothing disease mapping risks in large spatial data sets using INLA
(Elsevier, 2021)
Artículo / Artikulua,
Several methods have been proposed in the spatial statistics literature to analyse big data sets in continuous domains. However, new methods for analysing high-dimensional areal data are still scarce. Here, we propose a ...
A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis
(Elsevier, 2022)
Artículo / Artikulua,
Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling ...
Unpaired spatio-temporal fusion of image patches (USTFIP) from cloud covered images
(Elsevier, 2023)
Artículo / Artikulua,
Spatio-temporal image fusion aims to increase the frequency and resolution of multispectral satellite sensor images in a cost-effective manner. However, practical constraints on input data requirements and computational ...
Predicting cancer incidence in regions without population-based cancer registries using mortality
(Oxford University Press, 2023)
Artículo / Artikulua,
Cancer incidence numbers are routinely recorded by national or regional population-based cancer registries (PBCRs). However, in most southern European countries, the local PBCRs cover only a fraction of the country. ...
Logistic regression versus XGBoost for detecting burned areas using satellite images
(Springer, 2024)
Artículo / Artikulua,
Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant ...