The Bayesian Group Lasso for Confounded Spatial Data
Trevor J. Hefley (thefley@ksu.edu),
Mevin B. Hooten (mevin.hooten@colostate.edu),
Ephraim M. Hanks (hanks@psu.edu),
Robin E. Russell (rerussell@usgs.gov) and
Daniel P. Walsh (dwalsh@usgs.gov)
Additional contact information
Trevor J. Hefley: Kansas State University
Mevin B. Hooten: Colorado State University
Ephraim M. Hanks: Pennsylvania State University
Robin E. Russell: U.S. Geological Survey National Wildlife Health Center
Daniel P. Walsh: U.S. Geological Survey National Wildlife Health Center
Journal of Agricultural, Biological and Environmental Statistics, 2017, vol. 22, issue 1, No 3, 42-59
Abstract:
Abstract Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM. Supplementary materials accompanying this paper appear online.
Keywords: Collinearity; Dimension reduction; Generalized linear mixed model; Spatial confounding (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://link.springer.com/10.1007/s13253-016-0274-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:jagbes:v:22:y:2017:i:1:d:10.1007_s13253-016-0274-1
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/13253
DOI: 10.1007/s13253-016-0274-1
Access Statistics for this article
Journal of Agricultural, Biological and Environmental Statistics is currently edited by Stephen Buckland
More articles in Journal of Agricultural, Biological and Environmental Statistics from Springer, The International Biometric Society, American Statistical Association
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).