Exploring spatial nonlinearity using additive approximation

TL;DR

This paper introduces an additive approximation method for modeling nonlinear spatial data, capturing local dependencies without requiring ordering, and demonstrates its effectiveness through simulations and real data applications.

Contribution

It develops a novel additive estimation approach for spatial data that handles nonlinearity and local dependence, extending backfitting theory to spatial processes.

Findings

Additive estimators effectively capture nonlinear features in spatial data.

The method provides reliable confidence intervals for component functions.

Significant improvement over auto-normal models in non-Gaussian, nonlinear cases.

Abstract

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.