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Model selection for geostatistical models.

Jennifer A Hoeting1, Richard A Davis, Andrew A Merton

  • 1Department of Statistics, Colorado State University, Fort Collins, Colorado 80523-1877, USA. jah@lamar.colostate.edu

Ecological Applications : a Publication of the Ecological Society of America
|May 19, 2006
PubMed
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Ignoring spatial correlation in model selection for geospatial data can obscure variable importance. Using the Akaike Information Criterion (AIC) with geostatistical models improves explanatory variable selection compared to traditional methods.

Area of Science:

  • Geospatial statistics
  • Environmental modeling
  • Statistical ecology

Background:

  • Spatial correlation is frequently overlooked in geospatial data analysis, potentially leading to inaccurate variable selection.
  • Ignoring spatial dependencies can mask the true importance of explanatory variables in statistical models.
  • Traditional model selection methods may not adequately account for the unique characteristics of geospatial datasets.

Purpose of the Study:

  • To investigate the impact of spatial correlation on model selection for geospatial data.
  • To propose and evaluate the Akaike Information Criterion (AIC) for geostatistical model selection.
  • To compare geostatistical AIC-based selection with traditional approaches that ignore spatial structure.

Main Methods:

  • Heuristic derivation of the Akaike Information Criterion (AIC) for geostatistical models.

Related Experiment Videos

  • Simulation studies to assess the performance of AIC in geostatistical variable selection.
  • Application of AIC and Minimum Description Length (MDL) principles to a lizard abundance dataset.
  • Exploration of sampling design effects on covariate selection in geostatistical models.

    • Main Results:

    • Geostatistical AIC-based model selection significantly outperforms traditional methods that ignore spatial correlation.
    • The importance of explanatory variables is more accurately identified when spatial correlation is considered.
    • The Minimum Description Length (MDL) principle also shows promise for geostatistical variable selection.
    • Sampling design influences the selection of explanatory covariates.

    Conclusions:

    • The Akaike Information Criterion (AIC) is a superior tool for model selection in geostatistical analyses.
    • Accounting for spatial correlation is crucial for accurate variable importance assessment in geospatial data.
    • The developed methods and provided R software facilitate improved geospatial model selection.