Local Influence for Spatially Correlated Binomial Data: An Application to the Spodoptera frugiperda Infestation in Corn

D. T. Nava, F. De Bastiani, M. A. Uribe-Opazo, O. Nicolis, M. Galea

Research output: Contribution to journalArticlepeer-review

Abstract

Influence diagnostics are valuable tools for understanding the influence of data and/or model assumptions on the results of a statistical analysis. This paper proposes local influence for the analysis of spatially correlated binomial data. We consider a spatial model with a binomial marginal distribution and logit link function. Generalized estimating equations via Fisher’s scoring are used for estimating the parameters. We present an application to the spatial Spodoptera frugiperda infestation where the generalized estimating equations are used to identify potential influential observations by the local influence analysis. The spatial prediction with and without the influential points is compared. The results show that the presence of the influential observation in the data changes statistical inference, the predicted values and the respective maps. A simulation study considering different scenarios shows the performance of the local influence diagnostic method.

Original languageEnglish
Pages (from-to)540-561
Number of pages22
JournalJournal of Agricultural, Biological, and Environmental Statistics
Volume22
Issue number4
DOIs
Publication statusPublished - 1 Dec 2017

Keywords

  • Binomial distribution
  • Exponential family
  • Fisher’s score
  • Outliers
  • Quasi-likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Environmental Science(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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