Flexible models for nonstationary dependence: Methodology and examples
Abstract
There are many situations when modelling environmental phenomena for which it is not appropriate to assume a stationary dependence structure. \cite{sampson1992} proposed an approach to allowing nonstationarity in dependence based on a deformed space: coordinates from original geographic "" space are mapped to a new dispersion "" space in which stationary dependence is a reasonable assumption. \cite{sampson1992} achieve this with two deformation functions, which are chosen as thin plate splines, each representing how one of the two coordinates in -space relates to the original -space coordinates. This works extends the deformation approach, and the dimension expansion approach of \cite{bornn2012}, to a regression-based framework in which all dimensions in -space are treated as "smooths" as found, for example, in generalized additive models. The framework offers an intuitive and user-friendly approach to specifying -space, allows different levels of smoothing for dimensions in -space, and allows objective inference for all model parameters. Furthermore, a numerical approach is proposed to avoid non-bijective deformations, should they occur, which applies to any deformation. The proposed framework is demonstrated on the solar radiation data studied in \cite{sampson1992}, and then on an example related to risk analysis, which culminates in producing simulations of extreme rainfall for part of Colorado, US.
Cite
@article{arxiv.2001.06642,
title = {Flexible models for nonstationary dependence: Methodology and examples},
author = {Benjamin D. Youngman},
journal= {arXiv preprint arXiv:2001.06642},
year = {2020}
}