Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7496558 | Spatial Statistics | 2016 | 10 Pages |
Abstract
We propose a model-based geostatistical approach to deal with regionalized compositions. We combine the additive-log-ratio transformation with multivariate geostatistical models whose covariance matrix is adapted to take into account the correlation induced by the compositional structure. Such specification allows the usage of standard likelihood methods for parameters estimation. For spatial prediction we combined a back-transformation with the Gauss-Hermite method to approximate the conditional expectation of the compositions. We analyse particle size fractions of the top layer of a soil for agronomic purposes which are typically expressed as proportions of sand, clay and silt. Additionally a simulation study assesses the small sample properties of the maximum likelihood estimator.
Keywords
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Earth and Planetary Sciences (General)
Authors
Ana Beatriz Tozzo Martins, Wagner Hugo Bonat, Paulo Justiniano Jr.,