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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 ...
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 ...
Temporal evolution of brain cancer incidence in the municipalities of Navarre and the Basque Country, Spain
(BioMed Central, 2015)
Artículo / Artikulua,
Background: Brain cancer incidence rates in Spain are below the European’s average. However, there are two
regions in the north of the country, Navarre and the Basque Country, ranked among the European regions with ...
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. ...
In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results
(Springer, 2018)
Artículo / Artikulua,
Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on
generalized linear mixed models (GLMMs) including fixed
effects and spatial, temporal, and spatio-temporal ...
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 ...
Stochastic spatio-temporal models for analysing NDVI distribution of GIMMS NDVI3g images
(MDPI, 2017)
Artículo / Artikulua,
The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by ...