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J. Mateu  - - - 
Top co-authors See all
Vicent J. Martinez

99 shared publications

Unidad Asociada Observatorio Astronómico (IFCA-UV), E-46980 Valencia, Spain

David Pulido-Velazquez

71 shared publications

Departamento de Investigación en Recursos Geológicos, Instituto Geológico y Minero de España, Urb. Alcázar del Genil, 4. Edificio Zulema Bajo, 18006 Granada, Spain

Pedro Cabral

50 shared publications

NOVA IMS, New University of Lisbon, Lisbon, Portugal

Pedro Delicado

47 shared publications

Departament d’Estadística i Investigació Operativa, Universitat Politècnica d Catalunya, Barcelona, Spain

Enn Saar

46 shared publications

Tartu Observatoorium

Publication Record
Distribution of Articles published per year 
(2002 - 2018)
Total number of journals
published in
Publications See all
Article 0 Reads 0 Citations Resample-smoothing of Voronoi intensity estimators M. Mehdi Moradi, Ottmar Cronie, Ege Rubak, Raphael Lachieze-... Published: 19 January 2019
Statistics and Computing, doi: 10.1007/s11222-018-09850-0
DOI See at publisher website ABS Show/hide abstract
Voronoi estimators are non-parametric and adaptive estimators of the intensity of a point process. The intensity estimate at a given location is equal to the reciprocal of the size of the Voronoi/Dirichlet cell containing that location. Their major drawback is that they tend to paradoxically under-smooth the data in regions where the point density of the observed point pattern is high, and over-smooth where the point density is low. To remedy this behaviour, we propose to apply an additional smoothing operation to the Voronoi estimator, based on resampling the point pattern by independent random thinning. Through a simulation study we show that our resample-smoothing technique improves the estimation substantially. In addition, we study statistical properties such as unbiasedness and variance, and propose a rule-of-thumb and a data-driven cross-validation approach to choose the amount of smoothing to apply. Finally we apply our proposed intensity estimation scheme to two datasets: locations of pine saplings (planar point pattern) and motor vehicle traffic accidents (linear network point pattern).
Article 0 Reads 1 Citation Nonparametric tilted density function estimation:A cross-validation criterion Hassan Doosti, Peter Hall, Jorge Mateu Published: 01 December 2018
Journal of Statistical Planning and Inference, doi: 10.1016/j.jspi.2017.12.003
DOI See at publisher website
Article 0 Reads 2 Citations Equivalence and orthogonality of Gaussian measures on spheres Ahmed Arafat, Emilio Porcu, Moreno Bevilacqua, Jorge Mateu Published: 01 September 2018
Journal of Multivariate Analysis, doi: 10.1016/j.jmva.2018.05.005
DOI See at publisher website
Article 0 Reads 1 Citation Non-linear spatial modeling of rat sightings in relation to urban multi-source foci Carlos Ayyad, Jorge Mateu, Ibon Tamayo-Uria Published: 01 September 2018
Journal of Infection and Public Health, doi: 10.1016/j.jiph.2018.05.009
DOI See at publisher website
PREPRINT 0 Reads 0 Citations Resample-smoothing of Voronoi intensity estimators M. Mehdi Moradi, Ottmar Cronie, Ege Rubak, Raphael Lachieze-... Published: 07 July 2018
Article 0 Reads 2 Citations Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models Shivam Gupta, Edzer Pebesma, Jorge Mateu, Auriol Degbelo Published: 05 May 2018
Sustainability, doi: 10.3390/su10051442
DOI See at publisher website ABS Show/hide abstract
A very common curb of epidemiological studies for understanding the impact of air pollution on health is the quality of exposure data available. Many epidemiological studies rely on empirical modelling techniques, such as land use regression (LUR), to evaluate ambient air exposure. Previous studies have located monitoring stations in an ad hoc fashion, favouring their placement in traffic “hot spots”, or in areas deemed subjectively to be of interest to land use and population. However, ad-hoc placement of monitoring stations may lead to uninformed decisions for long-term exposure analysis. This paper introduces a systematic approach for identifying the location of air quality monitoring stations. It combines the flexibility of LUR with the ability to put weights on priority areas such as highly-populated regions, to minimise the spatial mean predictor error. Testing the approach over the study area has shown that it leads to a significant drop of the mean prediction error (99.87% without spatial weights; 99.94% with spatial weights in the study area). The results of this work can guide the selection of sites while expanding or creating air quality monitoring networks for robust LUR estimations with minimal prediction errors.