Geostatistical mixed beta regression: a Bayesian approach

This paper develops regression techniques for geostatistical data, with an emphasis in proportions measured on a continuous scale. Specifically, it deals with Beta regression models with mixed effects to control the spatial variability from a Bayesian approach. We use a suitable parametrization of t...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2017-02, Vol.31 (2), p.571-584
Main Authors: Lagos-Álvarez, Bernardo M., Fustos-Toribio, Roberto, Figueroa-Zúñiga, Jorge, Mateu, Jorge
Format: Article
Language:English
Subjects:
Aquatic Pollution
Bayesian analysis
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Ecological risk assessment
Environment
Geostatistics
Math. Appl. in Environmental Science
Mathematical models
Original Paper
Parameter estimation
Parameters
Physics
Probability Theory and Stochastic Processes
Regression
Regression analysis
Sampling
Specifications
Statistics for Engineering
Waste Water Technology
Water Management
Water Pollution Control
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Summary:This paper develops regression techniques for geostatistical data, with an emphasis in proportions measured on a continuous scale. Specifically, it deals with Beta regression models with mixed effects to control the spatial variability from a Bayesian approach. We use a suitable parametrization of the Beta distribution in terms of its mean and the precision parameter, allowing for both parameters to be modeled through regression structures that may involve fixed and random effects. Specification of prior distributions is discussed, computational implementation via Gibbs sampling is provided, and the methodology is illustrated using simulated and real data.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-016-1308-5