Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152955 | Statistics & Probability Letters | 2010 | 12 Pages |
Abstract
In this paper, we present a statistical framework for modeling conditional quantiles of spatial processes assumed to be strongly mixing in space. We establish the L1L1 consistency and the asymptotic normality of the kernel conditional quantile estimator in the case of random fields. We also define a nonparametric spatial predictor and illustrate the methodology used with some simulations.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Sophie Dabo-Niang, Baba Thiam,