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

Author

Listed:
  • D. T. Nava

    (Universidade Tecnológica Federal do Paraná)

  • F. De Bastiani

    (Universidade Federal de Pernambuco)

  • M. A. Uribe-Opazo

    (Universidade Estadual do Oeste do Paraná)

  • O. Nicolis

    (Universidad de Valparaíso)

  • M. Galea

    (Pontificia Universidad Católica de Chile)

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.

Suggested Citation

  • D. T. Nava & F. De Bastiani & M. A. Uribe-Opazo & O. Nicolis & M. Galea, 2017. "Local Influence for Spatially Correlated Binomial Data: An Application to the Spodoptera frugiperda Infestation in Corn," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 540-561, December.
    • Handle: RePEc:spr:jagbes:v:22:y:2017:i:4:d:10.1007_s13253-017-0306-5
    DOI: 10.1007/s13253-017-0306-5
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    References listed on IDEAS

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    1. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    2. Kang‐Mo Jung, 2008. "Local Influence in Generalized Estimating Equations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 286-294, June.
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    4. Miguel Angel Uribe-Opazo & Joelmir Andr� 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.
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    8. 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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