Coming up with Bayesian models for spatial data is easy, but performing inference with them
can be challenging. Writing fast inference code for a complex spatial model with realistically …

Geometrical deviation is an important factor in determining the quality of a three-dimensional
(3D) Surface. For 3D surfaces with complex shapes, the high-definition measurement (HDM) …

Diffusion MRI offers great potential in studying the human brain microstructure and
connectivity. However, diffusion images are marred by technical problems, such as image …

Supplement to “Strictly and non-strictly positive definite functions on spheres”. Appendix A
states and proves further criteria of Pólya type, thereby complementing Section 4.2 …

In this article, we propose stationary covariance functions for processes that evolve
temporally over a sphere, as well as cross-covariance functions for multivariate random …

A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space Page 1
Electronic Journal of Statistics Vol. 7 (2013) 2209–2240 ISSN: 1935-7524 DOI: 10.1214/13-EJS843 …

Introducing flexible covariance functions is critical for interpolating spatial data since the
properties of interpolated surfaces depend on the covariance function used for Kriging. An …

Ecosystems have always been shaped by disturbances, but many of these events are
becoming larger, more severe and more frequent. The recovery capacity of depleted …

Statistical models used in geophysical, environmental, and climate science applications
must reflect the curvature of the spatial domain in global data. Over the past few decades …

In the last years there has been a growing interest in proposing methods for estimating
covariance functions for geostatistical data. Among these, maximum likelihood estimators …