Title: Modelling function-valued processes with nonseparable covariance structure
Authors: Evandro Konzen - Newcastle University (United Kingdom) [presenting]
Jian Qing Shi - Newcastle University (United Kingdom)
Abstract: Separability of the covariance structure is a common assumption for function-valued processes defined on two- or higher-dimensional domains. This assumption is often made to obtain an interpretable model or due to difficulties in modelling a potentially complex covariance structure, especially in the case of sparse designs. We suggest using Gaussian processes with flexible parametric covariance kernels which allow interactions between the inputs in the covariance structure. When we use suitable covariance kernels, the leading eigensurfaces of the covariance operator can explain well the main modes of variation in the functional data, including the interactions between the inputs. The results are demonstrated by simulation studies and by an application to human fertility data.