Please login first
J. Mateu  - - - 
Top co-authors See all
Vicent J. Martínez

98 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

51 shared publications

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisbon, Portugal

Enn Saar

50 shared publications

Tartu Observatoorium

Pedro Delicado

46 shared publications

Universitat Politecnica de Catalunya

155
Publications
0
Reads
0
Downloads
145
Citations
Publication Record
Distribution of Articles published per year 
(2002 - 2018)
Total number of journals
published in
 
25
 
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 On Kernel-Based Intensity Estimation of Spatial Point Patterns on Linear Networks Mohammad Mehdi Moradi, Francisco Javier Rodríguez-Cortés, Jo... Published: 03 April 2018
Journal of Computational and Graphical Statistics, doi: 10.1080/10618600.2017.1360782
DOI See at publisher website
Top