The empirical variogram is a standard tool in the investigation and modelling of spatial covariance. However, its properties can be difficult to identify and exploit in the context of exploring the characteristics of individual datasets. This is particularly true when seeking to move beyond description towards inferential statements about the structure of the spatial covariance which may be pre...

Groundwater flow in a small watershed in a hard rock region of Andhra Pradesh, India mainly exists in a coupled system of weathered and fractured rock aquifers. However, due to heavy extraction, groundwater level has declined and the weathered part has become unsaturated. In general, the water-table aquifer exists in the area under semi-confined or unconfined conditions. Monthly water-levels fr...

This paper investigates the commonly overlooked “sensitivity” of sensitivity analysis (SA) to what we refer to as parameter “perturbation scale”, which can be defined as a prescribed size of the sensitivityrelated neighbourhood around any point in the parameter space (analogous to step size Dx for numerical estimation of derivatives). We discuss that perturbation scale is inherent to any (local...

A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostat...

Estimation of covariance function parameters of the error process in the presence of an unknown smooth trend is an important problem because solving it allows one to estimate the trend nonparametrically using a smoother corrected for dependence in the errors. Our work is motivated by spatial statistics but is applicable to other contexts where the dimension of the index set can exceed one. We o...

We propose here an interpolation method based on a decomposition of the data in largeand small-scale variation. This decomposition was performed using a two-way directional decomposition, similar to the decomposition used by Cressie in his median-polish kriging (1993), though we applied decomposition by means instead of medians. We considered the effects isolated by the decomposition as associa...

When modelling a large area, models that can take into a count the variation from the general mean in small sub-areas could perform better in prediction than a general model fitted to entire dataset. One method for adjusting the large-area models for such variation is kriging, in which the predictions are corrected with the aid of neighbouring observations. A variogram represents the spatial co...