Sandra De Iaco

Geostatistics and the Role of Variogram in Time Series Analysis: A Critical Review

Exploratory data analysis and prediction in time series modeling are not typically based on geostatistical techniques, although in several cases applying these techniques might be convenient.

Wind velocity prediction through complex kriging: formalism and computational aspects

The formalism of complex random fields is suitable for describing, in a compact and unified way, vectorial data sets in two dimensions, such as wind fields, electromagnetic fields, as well as measurements derived from any two dimensional vectorial field. This representation is rarely considered in Geostatistics, although interesting applications can be found in environmental sciences and meteorology. Moreover, the lack of procedures for modeling a complex covariance function as well as the lack of specialized routines for computing predictions in the complex case have considerably restricted the applications of these techniques. In this paper, some essential aspects of the complex formalism and some computational developments associated with fitting a complex covariance function and predicting a complex-valued random field through an ad-hoc routine are discussed. An environmental application to wind data is provided, moreover, the consistency of the proposed methodology is demonstrated through a numerical study.

Some Aspects of Multivariate Geostatistics

Cokriging allows the use of data on correlated variables in order to enhance the estimation of a primary variable or more generally to enhance the estimation of all variables. However, in order to apply the estimation procedure, some problems must be solved: the variogram matrix must be conditionally negative definite and it is necessary to model the cross-variogram. The aim of this paper is to present a flexible solution in order to solve the mentioned problems. A case study has been presented.