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Influence diagnostics in Gaussian spatial linear models

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  • Miguel Angel Uribe-Opazo
  • Joelmir André Borssoi
  • Manuel Galea

Abstract

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.

Suggested Citation

  • Miguel Angel Uribe-Opazo & Joelmir André Borssoi & Manuel Galea, 2012. "Influence diagnostics in Gaussian spatial linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 615-630, July.
  • Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:615-630
    DOI: 10.1080/02664763.2011.607802
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    File URL: http://hdl.handle.net/10.1080/02664763.2011.607802
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    1. repec:spr:jagbes:v:22:y:2017:i:4:d:10.1007_s13253-017-0306-5 is not listed on IDEAS
    2. Fernanda De Bastiani & Audrey Mariz de Aquino Cysneiros & Miguel Uribe-Opazo & Manuel Galea, 2015. "Influence diagnostics in elliptical spatial linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 322-340, June.

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