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A Sample Covariance-Based Approach For Spatial Binary Data

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  • Sahar Zarmehri

    (The Pennsylvania State University)

  • Ephraim M. Hanks

    (The Pennsylvania State University)

  • Lin Lin

    (The Pennsylvania State University)

Abstract

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.

Suggested Citation

  • Sahar Zarmehri & Ephraim M. Hanks & Lin Lin, 2021. "A Sample Covariance-Based Approach For Spatial Binary Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 220-249, June.
    • Handle: RePEc:spr:jagbes:v:26:y:2021:i:2:d:10.1007_s13253-020-00424-0
    DOI: 10.1007/s13253-020-00424-0
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    References listed on IDEAS

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    1. Pauline L. Kamath & Jeffrey T. Foster & Kevin P. Drees & Gordon Luikart & Christine Quance & Neil J. Anderson & P. Ryan Clarke & Eric K. Cole & Mark L. Drew & William H. Edwards & Jack C. Rhyan & John, 2016. "Genomics reveals historic and contemporary transmission dynamics of a bacterial disease among wildlife and livestock," Nature Communications, Nature, vol. 7(1), pages 1-10, September.
    2. Nicholas G. Polson & James G. Scott & Jesse Windle, 2013. "Bayesian Inference for Logistic Models Using Pólya--Gamma Latent Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1339-1349, December.
    3. Schliep, Erin M. & Hoeting, Jennifer A., 2015. "Data augmentation and parameter expansion for independent or spatially correlated ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 1-14.
    4. Carl, G. & Kühn, I., 2007. "Analyzing spatial autocorrelation in species distributions using Gaussian and logit models," Ecological Modelling, Elsevier, vol. 207(2), pages 159-170.
    5. Ephraim M. Hanks & Mevin B. Hooten, 2013. "Circuit Theory and Model-Based Inference for Landscape Connectivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 22-33, March.
    6. Peter Diggle & Rana Moyeed & Barry Rowlingson & Madeleine Thomson, 2002. "Childhood malaria in the Gambia: a case‐study in model‐based geostatistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 493-506, October.
    7. Alan E. Gelfand & Alexandra M. Schmidt & Shanshan Wu & John A. Silander & Andrew Latimer & Anthony G. Rebelo, 2005. "Modelling species diversity through species level hierarchical modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 1-20, January.
    8. Ephraim M. Hanks, 2017. "Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 497-507, April.
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