Exploring spatial nonlinearity using additive approximation

Details Exploring spatial nonlinearity using additive approximation Exploring spatial nonlinearity using additive approximation

2007-08-30

arxiv.org/abs/0708.4132v1

Bernoulli 2007, Vol. 13, No. 2, 447-472

Mathematics

Statistics Theory

Published at http://dx.doi.org/10.3150/07-BEJ5093 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statist

Scientific paper

10.3150/07-BEJ5093

We propose to approximate the conditional expectation of a spatial random variable given its nearest-neighbour observations by an additive function. The setting is meaningful in practice and requires no unilateral ordering. It is capable of catching nonlinear features in spatial data and exploring local dependence structures. Our approach is different from both Markov field methods and disjunctive kriging. The asymptotic properties of the additive estimators have been established for $\alpha$-mixing spatial processes by extending the theory of the backfitting procedure to the spatial case. This facilitates the confidence intervals for the component functions, although the asymptotic biases have to be estimated via (wild) bootstrap. Simulation results are reported. Applications to real data illustrate that the improvement in describing the data over the auto-normal scheme is significant when nonlinearity or non-Gaussianity is pronounced.

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