Journal of Statistical Theory and Practice

, Volume 8, Issue 2, pp 192–220 | Cite as

Conditional Hazard Estimate for Functional Random Fields

  • Ali LaksaciEmail author
  • Boubaker Mechab


We consider the problem of nonparametric estimation of the conditional hazard function for spatial data. More precisely, given a strictly stationary random field \({Z_{\rm{i}}} = {({X_{\rm{i}}},\;{Y_{\rm{i}}})_{{\rm{i}} \in {{\mathbb{N}}^N}}}\), we investigate a kernel estimate of the conditional hazard function of univariate response variable Yi given the functional variable Xi. The principal aims of this article are to give the mean squared convergence rate and to prove the asymptotic normality of the proposed estimator. Finally, a simulation study and an application on real data are carried out to illustrate, for finite samples, the behavior of our method.


Kernel conditional hazard function Kernel estimation Spatial process Functional data 


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Copyright information

© Grace Scientific Publishing 2014

Authors and Affiliations

  1. 1.Laboratoire de Statistique et Processus StochastiquesUniversité Djillali LiabèsSidi Bel AbbèsAlgeria

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