
Open Access

Subscription or Fee Access
Kernel Inverse Regression for random fields
Jean-Michel Loubes, Anne-Françoise Yao
Abstract
In this paper, we propose a Dimension Reduction model for spatially dependent variables. Namely, we investigate a generalization of the Inverse Regression method under some mixing conditions. This method introduced by (Li, 1991) for i.i.d. data is based on the estimation of the matrix of covariance of the conditional expectation of the explanatory variable given the response variable. Here, we investigate the weak consistency of this estimate based on a kernel estimate of the Inverse Regression under strong mixing condition. Through some simulations, we show the difference of behavior between our method and its i.i.d. counterpart. We also, investigate applications of our method in spatial forecasting problems and confront its with some others whom make their proof through a real data application.
Keywords
Kernel estimator, Spatial regression,Dimension reduction, Inverse Regression, Spatial forecasting
Full Text:
PDF
Refbacks
- There are currently no refbacks.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.