Influence diagnostics in Gaussian spatial linear models
Spatial linear models have been applied in numerous fields such as agriculture, geoscience and environmental sciences, among many others. Spatial dependence structure modelling, using a geostatistical approach, is an indispensable tool to estimate the parameters that define this structure. However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. The purpose of this paper is to use diagnostic techniques to assess the sensitivity of the maximum-likelihood estimators, covariance functions and linear predictor to small perturbations in the data and/or the spatial linear model assumptions. The methodology is illustrated with two real data sets. The results allowed us to conclude that the presence of atypical values in the sample data have a strong influence on thematic maps, changing the spatial dependence structure.
Year of publication: |
2012
|
---|---|
Authors: | Uribe-Opazo, Miguel Angel ; Borssoi, Joelmir André ; Galea, Manuel |
Published in: |
Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 39.2012, 3, p. 615-630
|
Publisher: |
Taylor & Francis Journals |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Uribe-Opazo, Miguel Angel, (2006)
-
Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data
Sánchez, Luis, (2020)
-
Robust inference in the capital asset pricing model using the multivariate t-distribution
Galea, Manuel, (2020)
- More ...