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

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 distrib...

Full description

Saved in:
Bibliographic Details
Published in:Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 2017-12, Vol.22 (4), p.540-561
Main Authors: Nava, D. T., De Bastiani, F., Uribe-Opazo, M. A., Nicolis, O., Galea, M.
Format: Article
Language:English
Subjects:
Agriculture
Biostatistics
Computer simulation
Corn
Correlation analysis
Data processing
Diagnostic systems
Estimation
Geospatial data
Health Sciences
Infestation
Mathematical models
Mathematics and Statistics
Medicine
Monitoring/Environmental Analysis
Parameter estimation
Predictions
Spatial analysis
Spatial data
Spatial distribution
Spodoptera frugiperda
Statistical analysis
Statistical inference
Statistics
Statistics for Life Sciences
Zea mays
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1085-7117
1537-2693
DOI:10.1007/s13253-017-0306-5