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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 ...
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. ...
Body composition and resting energy expenditure in a group of children with achondroplasia
(Elsevier, 2024)
Artículo / Artikulua,
Background: Persons with achondroplasia develop early obesity, which is a comorbidity associated with other complications. Currently, there are no validated specific predictive equations to estimate resting energy expenditure ...
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 ...
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 ...
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 ...
A scalable approach for short-term disease forecasting in high spatial resolution areal data
(Wiley-VCH, 2023)
Artículo / Artikulua,
Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be ...
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 ...