Co-clustering Spatial Data Using a Generalized Linear Mixed Model With Application to the Integrated Pest Management
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Co-clustering has been broadly applied to many domains such as bioinformatics and text mining. However, model-based spatial co-clustering has not been studied. In this paper, we develop a co-clustering method using a generalized linear mixed model for spatial data. To avoid the high computational demands associated with global optimization, we propose a heuristic optimization algorithm to search for a near optimal co-clustering. For an application pertinent to Integrated Pest Management, we combine the spatial co-clustering technique with a statistical inference method to make assessment of pest densities more accurate. We demonstrate the utility and power of our proposed pest assessment procedure through simulation studies and apply the procedure to studies of the persea mite (Oligonychus perseae), a pest of avocado trees, and the citricola scale (Coccus pseudomagnoliarum), a pest of citrus trees.
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- Co-clustering Spatial Data Using a Generalized Linear Mixed Model With Application to the Integrated Pest Management
Journal of Agricultural, Biological, and Environmental Statistics
Volume 17, Issue 2 , pp 265-282
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Heuristic optimization
- Integrated pest management
- Spatial co-clustering
- Author Affiliations
- 1. One Research Circle, GE Global Research, Niskayuna, NY, 12309, USA
- 2. Department of Statistics, University of California, 900 University Avenue, Riverside, CA, 92521, USA
- 3. Department of Entomology, University of California, 900 University Avenue, Riverside, CA, 92521, USA