Title :

A kernel spatial density estimation allowing for the analysis of spatial clustering. Application to Monsoon Asia Drought Atlas data

Author(s) :

Dabo, Sophie [Auteur]
Hamdad, Leila [Auteur]
Ternynck, Camille [Auteur]
Yao, Anne-Françoise [Auteur]

Journal title :

Stochastic Environmental Research and Risk Assessment

Volume number :

28

Pages :

2075-2099

Publisher :

Springer Verlag (Germany)

Publication date :

2014-12

ISSN :

1436-3240

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]

English abstract : [en]

A nonparametric density estimate that incorporates spatial dependency has not been studied in the literature. In this article, we propose a new spatial density estimator that depends on two kernels: one controls the distance ...
Show more >A nonparametric density estimate that incorporates spatial dependency has not been studied in the literature. In this article, we propose a new spatial density estimator that depends on two kernels: one controls the distance between observations while the other controls the spatial dependence structure. The uniform almost sure convergence of the density estimate is established with the rate of convergence. The consistency of the mode of this kernel density is also studied. Then a spatial hierarchical unsupervised clustering algorithm based on the mode estimate is presented. Some simulations as well as an application to the Monsoon Asia Drought Atlas data illustrate the efficiency of our algorithm, and a comparison of the spatial structures of these data detected by the density estimate and clustering algorithm are done.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CNRS
IESEG School of Management
Institut Catholique Lille
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2020-06-08T14:11:23Z
2021-06-24T12:57:09Z