Modelling Spatially Sampled Proportion Processes

Authors

  • Iosu Paradinas Universitat de València
  • Maria Grazia Pennino Instituto Español de Oceanografía
  • Antonio López-Quílez Universitat de València
  • Marcial Marín Universitat de València
  • José María Bellido Instituto Español de Oceanografía
  • David Conesa Universitat de València

DOI:

https://doi.org/10.57805/revstat.v16i1.233

Keywords:

modelling proportions, beta regression, spatial modelling, Bayesian hierarchical modelling

Abstract

Many ecological processes are measured as proportions and are spatially sampled. In all these cases the standard procedure has long been the transformation of proportional data with the arcsine square root or logit transformation, without considering the spatial correlation in any way. This paper presents a robust regression model to analyse this kind of data using a beta regression and including a spatially correlated term within the Bayesian framework. As a practical example, we apply the proposed approach to a spatio-temporally sampled fishery discard dataset.

Published

2018-02-07

How to Cite

Paradinas , I., Pennino , M. G., López-Quílez , A., Marín , M., Bellido , J. M., & Conesa , D. (2018). Modelling Spatially Sampled Proportion Processes. REVSTAT-Statistical Journal, 16(1), 71–86. https://doi.org/10.57805/revstat.v16i1.233

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