A Sample Covariance-Based Approach For Spatial Binary Data

Sahar Zarmehri, Ephraim M. Hanks, Lin Lin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an infectious disease. We investigate two genetic patterns in spatially referenced single-nucleotide polymorphisms (SNPs): isolation by distance and isolation by resistance. We model the data using a Generalized Linear Mixed effect Model (GLMM) with spatially referenced random effects and provide a novel approach for estimating parameters in spatial GLMMs. In this approach, we use the links between binary probit models and bivariate normal probabilities to directly compute the model-based covariance function for spatial binary data. Parameter estimation is based on minimizing sum of squared distance between the elements of sample covariance and model-based covariance matrices. We analyze data including Brucella Abortus SNPs from spatially referenced hosts in the Greater Yellowstone Ecosystem.

Original languageEnglish (US)
Pages (from-to)220-249
Number of pages30
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number2
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

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


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