Spatial Shrinkage Via the Product Independent Gaussian Process Prior

Abstract

We study the problem of sparse signal detection on a spatial domain. We propose a novel approach to model continuous signals that are sparse and piecewise smooth as product of independent Gaussian processes (PING) with a smooth covariance kernel. The smoothness of the PING process is ensured by the smoothness of the covariance kernels of Gaussian components in the product, and sparsity is controlled by the number of components. The bivariate kurtosis of the PING process shows more components in the product results in thicker tail and sharper peak at zero. The simulation results demonstrate the improvement in estimation using the PING prior over Gaussian process (GP) prior for different image regressions. We apply our method to a longitudinal MRI dataset to detect the regions that are affected by multiple sclerosis (MS) in the greatest magnitude through an image-on-scalar regression model. Due to huge dimensionality of these images, we transform the data into the spectral domain and develop methods to conduct computation in this domain. In our MS imaging study, the estimates from the PING model are more informative than those from the GP model.

Double soft-thresholded model for multi-group scalar on vector-valued image regression

“…Spatial dependence, however, is still not fully incorporated in this approach. Recently, Roy et al [2021] developed a prior that is suitable to estimate piece-wise sparse signals. In Kang et al [2018], a soft-thresholded Gaussian process prior was proposed for a related problem.…”