Consistency of a nonparametric conditional mode estimator for random fields

Sophie Dabo-Niang, Sidi Ould-Abdi (), Ahmedoune Ould-Abdi () and Aliou Diop ()

Statistical Methods & Applications, 2014, vol. 23, issue 1, 39 pages

Abstract: Given a stationary multidimensional spatial process $$\left\{ Z_{\mathbf{i}}=\left( X_{\mathbf{i}},\ Y_{\mathbf{i}}\right) \in \mathbb R ^d\right. \left. \times \mathbb R ,\mathbf{i}\in \mathbb Z ^{N}\right\} $$ Z i = X i , Y i ∈ R d × R , i ∈ Z N , we investigate a kernel estimate of the spatial conditional mode function of the response variable $$Y_{\mathbf{i}}$$ Y i given the explicative variable $$X_{\mathbf{i}}$$ X i . Consistency in $$L^p$$ L p norm and strong convergence of the kernel estimate are obtained when the sample considered is a $$\alpha $$ α -mixing sequence. An application to real data is given in order to illustrate the behavior of our methodology. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Kernel conditional mode estimation; Regression estimation; Spatial process (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10260-013-0239-2

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