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Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.

Lorenzo Cecconi1, Laura Grisotto2, Dolores Catelan3

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Summary
This summary is machine-generated.

Preferential sampling, where spatial processes and sampling locations are dependent, requires advanced geostatistical models. This study introduces a Bayesian approach to address this violation, improving spatial predictions.

Keywords:
Bayesian geostatisticsPreferential samplingshared component model

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Area of Science:

  • Geostatistics
  • Spatial Statistics
  • Bayesian Modeling

Background:

  • Preferential sampling occurs when spatial processes and sampling locations are not independent.
  • This violates standard assumptions in geostatistical analysis.
  • Existing methods may produce biased results when preferential sampling is present.

Purpose of the Study:

  • To develop and present a flexible Bayesian geostatistical model to account for preferential sampling.
  • To demonstrate the application of this model in case studies.
  • To highlight key aspects of geostatistical modeling under preferential sampling.

Main Methods:

  • Specification of a general Bayesian geostatistical model incorporating a shared spatial random component.
  • Application of the model to two distinct case studies.
  • Analysis considering continuous or finite spatial sampling frames, causal models with covariates, and prediction uncertainty.

Main Results:

  • The proposed Bayesian model effectively accommodates the violation of stochastic independence between spatial and measurement processes.
  • The case studies illustrate the model's utility in handling different sampling scenarios and inferential goals.
  • The model provides a framework for more robust geostatistical predictions in the presence of preferential sampling.

Conclusions:

  • The developed Bayesian geostatistical model offers a flexible and powerful approach to address preferential sampling.
  • Accurate spatial prediction and uncertainty quantification are achievable even when sampling is not independent of the underlying process.
  • This work provides valuable tools for researchers dealing with non-independent spatial data.