Covariance functions and variograms play a fundamental role in exploratory analysis and statistical modelling of spatial and spatio-temporal datasets. In this paper, we construct a new class of spatial covariance functions using the Fourier transform of some higher-order kernels. Moreover, we extend this class of spatial covariance functions to the spatio-temporal setting using the idea used in...

casimir forces of massive ferrnionic dirac fields are calculated for parallel plates geometry in spatial space with dimension d and imposing bag model boundary conditions. it is shown that in the range of ma>>l where m is mass of fields quanta and a is the separation distance of the plates, it is equal to massive bosonic fields casimir force for each degree of freedom. we argue this equality ex...

Estimating the amount of variation due to spatial dependence at different scales provides a basis for designing effective experiments. Accurate knowledge of spatial structures is needed to inform silvicultural guidelines and management decisions for long term sustainability of forests. Furthermore, geostatistics is a useful tool to describe and draw map the spatial variability and estimation o...

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

mapping based on total concentrations of micronutrients may be useful in fertility management including fertilizer and/or wastewater application. the aims of this work were to (1) investigate the spatial distribution of total concentrations of copper (cu), zinc (zn), manganese (mn) and iron (fe); (2) to select the suitable spatial estimator; and (3) to understand the effect of wastewater irriga...

Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here]