Dans cette note, nous proposons un estimateur non paramétrique spatial de la fonction de régression , d'un champ stationnaire de dimension , à un point localisé à un site donné j. L'estimateur proposé est composé de deux noyaux permettant de contrôler à la fois la distance entre les observations et entre les sites. La convergence presque complète ainsi que la convergence en moyenne d'ordre q (norme ) de l'estimateur à noyaux sont obtenus en considérant des processus α-mélangeants.
In this note, we propose a nonparametric spatial estimator of the regression function , of a stationary -dimensional spatial process , at a point located at some station j. The proposed estimator depends on two kernels in order to control both the distance between observations and the spatial locations. Almost complete convergence and consistency in norm of the kernel estimate are obtained when the sample considered is an α-mixing sequence.
Accepté le :
Publié le :
@article{CRMATH_2015__353_7_635_0, author = {Dabo-Niang, Sophie and Ternynck, Camille and Yao, Anne-Francoise}, title = {A new spatial regression estimator in the multivariate context}, journal = {Comptes Rendus. Math\'ematique}, pages = {635--639}, publisher = {Elsevier}, volume = {353}, number = {7}, year = {2015}, doi = {10.1016/j.crma.2015.04.004}, language = {en}, url = {http://www.numdam.org/articles/10.1016/j.crma.2015.04.004/} }
TY - JOUR AU - Dabo-Niang, Sophie AU - Ternynck, Camille AU - Yao, Anne-Francoise TI - A new spatial regression estimator in the multivariate context JO - Comptes Rendus. Mathématique PY - 2015 SP - 635 EP - 639 VL - 353 IS - 7 PB - Elsevier UR - http://www.numdam.org/articles/10.1016/j.crma.2015.04.004/ DO - 10.1016/j.crma.2015.04.004 LA - en ID - CRMATH_2015__353_7_635_0 ER -
%0 Journal Article %A Dabo-Niang, Sophie %A Ternynck, Camille %A Yao, Anne-Francoise %T A new spatial regression estimator in the multivariate context %J Comptes Rendus. Mathématique %D 2015 %P 635-639 %V 353 %N 7 %I Elsevier %U http://www.numdam.org/articles/10.1016/j.crma.2015.04.004/ %R 10.1016/j.crma.2015.04.004 %G en %F CRMATH_2015__353_7_635_0
Dabo-Niang, Sophie; Ternynck, Camille; Yao, Anne-Francoise. A new spatial regression estimator in the multivariate context. Comptes Rendus. Mathématique, Tome 353 (2015) no. 7, pp. 635-639. doi : 10.1016/j.crma.2015.04.004. http://www.numdam.org/articles/10.1016/j.crma.2015.04.004/
[1] Nonparametric spatial prediction, Stat. Inference Stoch. Process., Volume 7 (2004) no. 3, pp. 327-349
[2] Kernel density estimation for random fields (density estimation for random fields), Stat. Probab. Lett., Volume 36 (1997) no. 2, pp. 115-125
[3] On the sphere problem, Rev. Mat. Iberoam., Volume 11 (1995) no. 2, pp. 417-430
[4] A kernel spatial density estimation allowing for the analysis of spatial clustering: application to Monsoon Asia Drought Atlas data, Stoch. Environ. Res. Risk Assess., Volume 28 (2014), pp. 2075-2099
[5] S. Dabo-Niang, C. Ternynck, A.-F. Yao, Nonparametric prediction of spatial multivariate data, 2015, preprint.
[6] Kernel regression estimation for continuous spatial processes, Math. Methods Stat., Volume 16 (2007) no. 4, pp. 298-317
[7] Nonparametric regression estimation for random fields in a fixed-design, Stat. Inference Stoch. Process., Volume 10 (2007) no. 1, pp. 29-47
[8] Asymptotic normality of the Parzen–Rosenblatt density estimator for strongly mixing random fields, Stat. Inference Stoch. Process., Volume 14 (2011) no. 1, pp. 73-84
[9] Asymptotic normality of kernel estimates in a regression model for random fields, J. Nonparametr. Stat., Volume 22 (2010) no. 8, pp. 955-971
[10] Moment inequalities for spatial processes, Stat. Probab. Lett., Volume 78 (2008) no. 6, pp. 687-697
[11] Nonparametric spatial prediction under stochastic sampling design, J. Nonparametr. Stat., Volume 22 (2010) no. 3, pp. 363-377
[12] Convergence of block spins defined by a random field, J. Stat. Phys., Volume 22 (1980) no. 6, pp. 673-684
[13] Stationary Sequences and Random Fields, Birkhäuser, Boston, 1985
[14] On the rates in the central limit theorem for weakly dependent random fields, Z. Wahrscheinlichkeitstheor. Verw. Geb., Volume 64 (1983) no. 4, pp. 445-456
[15] Spatial regression estimation for functional data with spatial dependency, J. Soc. Fr. Stat., Volume 155 (2014) no. 2, pp. 138-160
[16] Prediction for spatio-temporal models with autoregression in errors, J. Nonparametr. Stat., Volume 24 (2012) no. 1, pp. 217-244
Cité par Sources :